# SoK: An Overview of PPG’s Application in Authentication

Lin Li1, Chao Chen2, Lei Pan3, Jun Zhang1, and Yang Xiang1  1Swinburne
University of Technology, Australia  2James Cook University, Australia
3Deakin University, Australia

###### Abstract

Biometric authentication prospered during the 2010s. Vulnerability to spoofing
attacks remains an inherent problem with traditional biometrics. Recently,
unobservable physiological signals (e.g., Electroencephalography,
Photoplethysmography, Electrocardiography) as biometrics have been considered
a potential solution to this problem. In particular, Photoplethysmography
(PPG) measures the change of blood flow of the human body by an optical
method. Clinically, researchers commonly use PPG signals to obtain patients’
blood oxygen saturation, heart rate, and other information to assist in
diagnosing heart-related diseases. Since PPG signals are easy to obtain and
contain a wealth of individual cardiac information, researchers have begun to
explore its potential applications in information security. The unique
advantages (simple acquisition, difficult to steal, and live detection) of the
PPG signal allow it to improve the security and usability of the
authentication in various aspects. However, the research on PPG-based
authentication is still in its infancy. The lack of systematization hinders
new research in this field. We conduct a comprehensive study of PPG-based
authentication and discuss these applications’ limitations before pointing out
future research directions.

###### Index Terms:

Biometrics, Authentication, Physiological Signals, Photoplethysmography

##  I Introduction

Authentication ensures the legitimacy of access to data [1], the identity of
individuals and the integrity of messages. Authentication is useful in our
lives, including commercial applications, healthcare, access control, etc.

There are three varieties of authentication — knowledge-based authentication
like passwords, object-based authentication like ID cards, and biometric-based
authentication like face recognition [2]. Biometric-based authentication uses
physiological or behavioural characteristics extracted from a person as a
source of idiosyncratic information [3]. It does not suffer from being
forgotten or easily stolen comparing with knowledge- and object-based methods.
Since each individual has many idiosyncratically physical or behavioural
characteristics, a wealth of individual information can be leveraged to make
biometric-based authentication hard to be fabricated. The traditional features
used for biometrics include fingerprint, face, iris, voice, palmprint, and
many more [4]. In the 2010s, traditional biometric authentication thrived, for
example, using face recognition to unlock a smartphone and fingerprint
recognition to unlock a door. Nevertheless, these traditional biometric
authentication are vulnerable to replay and presentation attacks [5, 6].

Physiological signals are used in recent studies as biometrics that are not
readily observable, like Electroencephalography, Electrocardiography, and
Photoplethysmography (PPG) [7, 8, 9]. Specifically, a Pentagon’s product uses
infrared lasers to detect people’s unique heart features to authenticate
individuals [10]; a Canadian company Nymi has developed an authentication
system using wrist-worn pulse sensors as an alternative to fingerprint
recognition [11]. Different from traditional biological features,
physiological signal-based features are invisible on the human body’s surface,
making it challenging to be collected and analyzed by attackers.

Among the physiological signals, PPG is a non-invasive optical method for
measuring the volume of light absorbed or reflected by microvascular in
biological tissues [12]. Furthermore, PPG has a wide range of research
prospects in authentication due to its unique advantages: Simple
acquisition—An oximeter or a camera is all we need to capture PPG signals from
a human body, and similar signals can be captured from the face, fingertips,
or toes. In addition, the integration of PPG sensors into wearable devices
simplifies and reduces the cost of PPG signal acquisition. Difficult to
steal—Traditional biometrics are subject to many easy attacks Fingerprints and
palmprints can be copied from surfaces touched by a user [13]; iris and face
images can be taken at a distance. In contrast, PPG signals are not directly
exposed to the attacker, making them difficult to spoof. Live detection—The
liveliness of the users involved in the system is ensured by the natural
liveness detection system because the PPG signal responds to the information
of the human heartbeat.

Figure 1: Four-layered PPG-based authentication framework. Firstly, the PPG
signal of the user can be captured using different devices. Then, the raw PPG
signal is processed by signal denoising to obtain a high-quality signal. In
the third layer, features are extracted from the processed signal. Finally,
each of these features is applied to different tasks according to their
properties. For example, user authentication and key distribution Scheme.

PPG signals are not easy to be directly used for authentication. For example,
human heartbeats can be affected by many genetic and non-genetic factors,
according to many PPG signal studies [14, 15, 16]. Differences in PPG signals
are observed between individuals, implying their use in user authentication to
replace pre-set passwords with PPG signals acquired from the human body. PPG
signals were first applied in biometrics in 2003 [17]. Subsequently, the
derivatives of the PPG signal were also used for biometric authentication
[18]. The approach to individual’s feature matching has shifted from the
initial calculation of the distance between features to deep learning
classifiers [19]. In recent years, the potential of PPG signals in other
authentication scenarios has been explored as well [20, 21, 22]. PPG signals
can be measured from different body parts. This property allows us to use PPG
signals without key pre-distribution to encrypt device communication in an
environment with strict resource constraints like a Body Area Sensor Network.

A survey of heart biometrics was presented in [23] in the context of heart
signals for user authentication, but it suffers from a superficial coverage in
PPG signals with six papers. A review on wearable biometric systems was
provided in [24] with only a few acquisition methods for PPG signals. We
attempt to comprehensively investigate PPG signals in cybersecurity
applications. The main contributions are summarized as follows:

  * •

We systematically present PPG-based authentication associated with possible
security threats. We propose a novel taxonomy to organize various systems from
the technical and application perspectives to provide a comprehensive insight
into PPG signals.

  * •

We survey the most recent research on PPG-based authentication from 2003 to
2021 and summarize the view to enable future researchers to apply the PPG
signals technologies.

  * •

We discuss the challenges of PPG-based authentication to highlight open issues
for immediate attention and suggest possible countermeasures for future
research.

The rest of this paper is organized as follows: We propose a four-layered view
of PPG-based authentication in Section II. In Sections III and IV, the
literature review is presented on PPG-based authentication. Section V
discusses the challenges faced by PPG-based authentication and proposes the
corresponding future directions. Section VI concludes this paper.

##  II Four-Layered view on PPG-based authentication

In this section, we present a novel and generalized view for PPG-based
authentication. Fig. 1 presents a four-layered framework. The bottom layer is
the signal acquisition layer that collects PPG signals. The second layer
denoises the signal by enhancing its signal-to-noise ratio. The third layer,
called the PPG representation layer, extracts the signal’s features through
feature transformation and selection. The security application layer finally
uses the extracted features for authentication.

###  II-A Signal Acquisition Layer

This layer includes the actions for capturing the user’s PPG signal. It
extracts PPG signals from the skin and converts them into electrical signals
for transmission to the next layer. This layer consists of four main modules —
light source, sensor, skin, and storage. The blood flow in the skin is the
source of the signal. The light source exposes the signal to the sensor. The
sensor converts the received signal into an electrical signal to feed
subsequent layers for processing. The mainstream sensors are photodetectors.
It can convert the received light intensity into a voltage signal. A camera is
a sensor that captures richer information about light. Storage determines the
carrier of the signal, including electrical and video signals. Eventually, all
signals are transformed into PPG waveforms and passed to the next layer. In
this layer, signal quality is the most important characteristics. It
determines whether the PPG signal can provide us with authentication-related
information.

Depending on the sensor and acquisition method, there are four mainstream
methods to capture PPG signals, including oximetry, wearable devices,
smartphone cameras, HD cameras, and many more. The oximeter and wearable
device capture reflected or projected light intensity changes primarily
through light-sensitive sensors [25, 26]. Smartphone cameras and HD cameras
capture the change of RGB value among video frames to detect the change of
blood flow in human tissue [27, 28]. As shown in Fig. 2, we compared these
four methods in five dimensions.

The pulse oximeter obtains high-quality signals partly because it isolates the
interference from external ambient light. However, it needs to be clipped to a
human finger, which interferes with any tasks that require finger involvement
during continuous authentication. Having very little computational capability,
oximeters transmit the captured signals to the endpoint for processing,
increasing the risk of compromise. Wearable devices provide a new mode of
interaction without affecting individuals’ everyday lives, enabling continuous
unnoticed authentication. A built-in physiological signal sensor allows
wearable devices to capture PPG signals.

Unlike the traditional acquisition of PPG signals via photodetector, a phone
camera acquires PPG signals by using the flash light as the light source and
covering the fingertip on the camera [29]. The HD camera method analyzes the
face video for non-contact physiological measurements [30, 28, 31], while the
illumination usually comes from ambient light. However, the PPG signals
acquired using the camera is often low quality and noisy, especially for
people with dark skin tones and quick motion artifacts due to body movement
[32]. In addition, the surrounding light conditions can significantly affect
the signal quality. With the popularity of smartphones, HD cameras have been
built into various devices, so there is no extra cost with this acquisition
method. For security reasons, the HD camera approach allows remote acquisition
of PPG signals, which can be stolen by a malicious attacker without the
victim’s awareness.

Figure 2: Characteristics of different acquisition methods. We compared the
four acquisition methods on five dimensions. Regarding security, smartphones
and wearable devices performed the best. Regarding the signal quality, the
oximeter scored the highest. Phone cameras cost the least. HD cameras can
capture PPG signals at a distance. Phone cameras and Wearable devices have
excellent mobility.

###  II-B Noise Reduction Layer

In the acquisition of any biomedical signal, it is always accompanied by a
variety of noise, and noise will always reduce the quality of PPG signals
[33]. In this layer, the raw PPG signal is the input, and a high-quality PPG
signal is the output. Processing the noise in the signal is the primary
concern of this layer. We need to identify the types of noise present in the
signal before reducing it accordingly. The PPG signal contains rich heart-
related information, and it is challenging to determine the signal quality
directly. Human bodies are usually assessed through statistical indicators
(e.g., heartbeat interval, systolic peak) or physiological values (e.g.,
heartbeat rate, heart rate variability).

There are four primary types of noises, including low-frequency noise, high-
frequency noise, cardiac arrhythmia noise, and low amplitude PPG signals [34].
High- and low-frequency noise is more commonly present in PPG signals than the
other two.

Motion artifacts (MA) are the most common low-frequency noise typically found
in wearable devices. [35]. Both tissue deformation and sensor displacement may
cause the appearance of these artifacts [36]. Another type of low-frequency
noise is baseline wander noise. Under normal circumstances, the centerline of
the pulse wave signal is relatively smooth, indicating the signal’s DC
component is stable. However, the acquired signal has a constantly changing
amplitude value of the overall waveform due to BW caused by multiple factors,
such as temperature variations, the bias of the instrumentation amplifier, and
breathing motion [37]. High-frequency noise is normally caused by power line
interference that refers to the ambient electromagnetic signal of the
instrument amplifier and the power supply obstruction of the PPG recording
probe [34]. We can filter all the high-/low- frequency signals directly by
using the low-/high- pass filter, but it will discard a significant portion of
the original signal.

Figure 3: First and Second Derivative waveform of original PPG signals and the
physiologically significant features.

###  II-C PPG Representation Layer

This layer’s input is the cleansed signal, and its output is feature vectors
that are applicable to authentication systems. In this layer, the main
challenge is to extract features from the clean signal robust to time and
emotion and ensure the uniqueness of individual features. The feature capture
layer consists of two components — feature transformation and feature
selection.

We can directly extract the traditional fiducial points or statistical
information from the signal as our feature vectors, like systolic peak,
diastolic peak, and heart rate variability. Fig. 3 lists many features that
are used to diagnose heart-related diseases. For instance, diacrotic notch is
related to blood pressure [38]; systolic peak is associated with
cardiovascular aging [39]. Although these features can be acquired quickly
from the raw signal [40, 41], they are susceptible to changes in the
surrounding environment and the physical state of the subject.

Feature transformation converts the current feature space to a different space
to acquire robust features for the authentication, like from time-domain to
frequency-domain. Feature selection helps remove redundant or irrelevant
information. While removing the interference of useless information, feature
selection also reduces the dimensionality of features and the cost of
computation.

###  II-D Security Application Layer

In this layer, we will use the features extracted in the previous layer to
apply in different applications. It is mainly used in the authentication
system.

####  II-D1 User Authentication

PPG signals represent an individual’s unique hemodynamic and cardiovascular
system. Thus, a human’s PPG signal works as a unique identifier for user
authentication. Instead of the raw signals, the feature vectors are used in
user authentication. The user authentication process consists of two phases —
the enrollment (training) phase and the authentication phase. During the
enrollment phase, the biometric system learns the feature vectors extracted
from the individual. The learned templates will be stored locally on the
device or the cloud as individual identifiers. The authentication phase is
divided into two scenarios — verification and identification. Verification
only determines whether the user is consistent with their declared identity.
Identification requires finding the enrollment template in the system that
corresponds to the user. A biometric system can be regarded as a matching or
classification problem.

At present, several methods distinguish the PPG signals of different
individuals. A straightforward method uses the similarity between features to
distinguish the PPG signals between individuals. A predefined threshold value
determines the degree of similarity. If the similarity between features
exceeds a preset threshold, the signals are considered to be originated from
the same individual. Distance and correlation are usually used to discern
similarity [42, 43, 18, 44].

In machine learning-based methods, user authentication is translated to a
classification problem, where users are classified to their groups. Features
manually extracted through traditional machine learning do not guarantee an
adequate representation of the uniqueness of individual PPG signals. Deep
learning approaches are usually end-to-end solutions. Deep learning methods
feed the training set and corresponding labels into the model before learning
the training set features and inferring the test set results. When the
understanding of the feature domain is inadequate, deep learning methods are
often preferred.

Figure 4: Key exchange based on biometrics. Initially, the sender collects the
user’s PPG signal in synchronization with the receiver. Then, the sender uses
this signal as an entity identifier to hide the generated key. Later the hided
key is distributed to the receiver using some scheme. The receiver can use the
captured PPG signal to unlock and obtain the key.

####  II-D2 Other Authentication Scenarios

Other authentication scenarios take advantage of PPG signals’ variability
between individuals to generate entity identifiers [45, 21, 20, 22, 46]. The
entity identifier is subsequently used to identify the origin and authenticity
of messages. Meanwhile, the randomness of PPG signals is applicable to
encryption [47, 48, 49, 50, 51, 52]. In a body area sensor network (BASN),
symmetric key distribution scheme based on PPG signals has a unique advantage
under strict energy and memory constraints. Unlike other symmetric encryption
schemes, pre key-agreement is not required for BASN because the PPG signals
are measured at different parts of an individual. As shown in Fig. 4, the
sender and the receiver simultaneously measure the PPG signals, when the
sender uses the PPG signals to obfuscate the generated key. Subsequently, the
sender sends the key to the receiver through a key distribution scheme, while
the receiver can use his/her PPG signal to recover the message’s decryption
key.

However, the measured PPG signals may differ slightly due to various distances
from the heart at different sites. It is challenging for the signals to be
measured synchronously between devices. When used for encryption, the message
symbols are reordered, resulting in translation and rotation errors that
produce very different values. Traditionally, these differences can be treated
as errors in communication messages, and some simple error correction codes
can rectify the discrepancy, for instance, the hamming code error correction
function [51]. Although hamming code can correct some existing discrepancies
in the feature vector, it cannot handle the reordering of the feature vector
or the presence of additional features. Recently, the schemes like fuzzy
commitment [53, 20] and fuzzy vaults [48, 49, 50, 52] have been widely used in
biosignal-related security solutions.

##  III PPG-based User Authentication

In this section, we review all PPG-related papers on user authentication. This
section consists of three subsections: Section III-A describes the method for
obtaining high-quality signals, while the research in Section III-B introduces
constructing the representation of individual features. And Section III-C
describes the authentication model.

###  III-A Acquisition and Noise Reduction

The pulse signals in the PPG signals are repetitive waveforms, while most MA
is bursty signals. Hence, the statistical differences (e.g., kurtosis,
skewness, and standard deviation) in PPG signals can be used to detect MA
[54]. According to recoverability to cardiac signals, MA can be divided into
two categories — distal and proximal wrist [55]. Distal wrist activity is the
primary arm movement and does not involve the tendons and muscles of the wrist
region. In comparison, proximal wrist activities are horizontal and wrist-
level movements that directly affect blood volume changes in the wrist region
and significantly impact PPG measurements from wearable devices. In general,
proximal wrist activity can have a long-lasting, intense and non-recoverable
effect on PPG measurements, while distal wrist activity has a smaller and
recoverable effect on PPG signals. When MA is detected in many consecutive PPG
signals, the MA is attributed to proximal wrist activity, performs MA removal
to eliminate the affected PPG segments. When MA is detected in scattered or
only a few contiguous segments, it is associated with distant wrist activity,
we can reconstruct the associated pulse waveform. Continuous near-wrist
activity and accidental disease may cause sharp changes in heart condition and
affect the system’s performance, resulting in a temporary switch back to a
traditional authentication method like passwords.

With the popularity of mobile devices, mobile phone cameras have also been
studied for PPG signals acquisition [56]. However, the method of collecting by
mobile phone camera is easily affected by the pressing position and pressure
of fingertips and light conditions. Reliable cardiac motion patterns could
only be obtained with the proper camera configuration and sufficient light
entering the camera. Too low or too high flashlight illumination can reduce
pixel sensitivity when capturing cardiac motion patterns from the camera.
Thus, we need to adjust the camera configuration (i.e., flash intensity, ISO)
to complement the variation of ambient light [57]. Dynamically selecting the
pixels in the video captured by the camera, such as only a subset of most
sensitive pixels to heart motion or removing invalid pixel points, can improve
the signal-to-noise ratio of heart measurements.

Since PPG sensors consist of LED and photodetector with specific spectral
sensitivity and emission wavelengths, subtle differences in such devices are
common. These signals collected from different devices can be considered as
data from different domains. This problem can be handled by applying cross-
domain adaptation methods [58]. It is possible to eliminate the DC component
of the signal by adding an amplifier bias adjustment circuit, obtaining a high
signal-to-noise ratio AC component from the original PPG signal [59].
Improvements from a hardware perspective result in better signal quality and
make identification data processing easier.

Furthermore, the PPG signals are also influenced by internal factors of the
human body. In some current studies, data were obtained while the participant
was sitting steadily. They ignored the effects of exercise on PPG signals in
real life. Significant differences in the PPG signals were collected by
different participants in the exercise state [42]. Besides exercises, the PPG
signal reflects the influence of the autonomic nervous system on cardiac
activity, it can easily be altered by changes in heart rate caused by mood
fluctuations. Using a Gaussian function to represent the PPG signal features
approximately has excellent robustness for emotions [60]. Although the above
methods have investigated the influence of emotions in PPG-based user
authentication systems, the classification of emotions in their datasets is
based on participants’ subjective perceptions. It does not objectively reflect
the real situation.

As an authentication feature, feasibility is very important in long-term
situations. The correlation coefficients of the PPG waveforms recorded during
the month compared in [61] remain constant. Evaluations show that the
performance of the authentication model in the cross-session case will decline
over time [62, 9, 63, 64]. Feature selection can help to select features that
are less likely to vary with time [65]. Both model fusion and GAN can be used
to improve the stability of the model over time. [63, 64]. In continuous
authentication, there is no need to consider the effect of time on the signal
[66, 67].

###  III-B Representation Construction

Individual template vectors can be built by extracting the number of peaks,
time intervals and up and down slopes as features from a single-cycle PPG
signal [17]. Based on the above work, morphological features like the waveform
area and the waveform angle were introduced in [68]. The features are taken
directly from the original waveform, implying its vulnerability is subject to
the interference of external factors (baseline wander, motion artifact). This
method of approximating the signal ignores the information of higher-order
derivatives contained in the pulse. The information of the PPG signals cannot
be fully utilized to improve recognition accuracy and reliability. For this
reason, the first- and second-order derivatives of the PPG signals are
utilized [18]. The features obtained through higher-order derivatives are
discriminative and sensitive to noise in the recognition task. In contrast,
features from lower-order derivatives are more robust and less sensitive than
the higher-order counterparts.

The human gestures will cause changes in blood flow in the blood vessels [69,
70]. The peak of the PPG signals can detect the motion information of the hand
to assist authentication. Compared with other personal authentication methods
that use motion sensors on smartwatches, this method identifies individuals
with only a few small wrist movements in a short period like bending the
wrist.

The feature transformation can obtain robust individual template vectors.
Fourier transform converts the signal from the time domain to the frequency
domain [64]. Frequency-domain signals are generally regarded to be robust to
time variations than time-domain signals. However, the Fourier transform has
an inherent flaw when dealing with non-smooth signals. It can only obtain
information about which frequency components a segment of the signal contains
in general, instead of the moments when each component appears. Thus two
signals with very different time domains may have the same spectrogram. In
this case, Short-time Fourier Transform can decompose the entire time domain
of the signal into an infinite number of small processes of equal length [71].
By setting the window length, we can obtain the frequency at a particular
point in time. Nevertheless, it cannot meet the demand of the changing
frequency of non-stationary signals as PPG signals. The components of various
signals in nature at different frequencies have different time-varying
characteristics. Generally, the spectral features of the lower frequency
components change more slowly with time, while the higher frequency features
change more rapidly. To obtain suitable frequency resolution and time
resolution in different time-frequency regions, [61] uses Wavelet Transform to
decompose the signal into various parts located in different time and
frequency bands. Mel-Frequency Cepstral Coefficients is also possible to work
on certain specific frequency components according to the nonlinear Mel scale
[72].

The waveform of a heartbeat can be described by some simple function [73, 74].
We can use some morphological modelling approaches to describe the PPG signals
for biometrics quantitatively [75]. In the above method, the data must be pre-
processed and features are extracted manually. Deep learning can be used to
develop a fully data-driven end-to-end biometric system based on PPG [76, 77].

In the process of constructing individual template vectors, feature selection
further improves the discriminability and robustness of the features.
Principal Component Analysis is a commonly used method for statistical
analysis and simplification of features. Principal Component Analysis was used
in [78] to remove correlations between variables in a biometric system,
retaining key features that effectively distinguish PPG signals from different
individuals. However, Principal Component Analysis only performs linear
transformations on the data, resulting in unfavourable for linearly
inseparable data. Kernel Principal Component Analysis is proposed to map data
that cannot be linearly classified in the low-dimensional space to the high-
dimensional space for principal component analysis [79]. In addition, various
algorithms are used for feature selection in biometric systems, including
linear discriminant analysis [65], forward feature selection [80], relief
algorithm [81], and genetic algorithm [82].

###  III-C Authentication Model

Among the similarity-based methods for identifying individual templates, the
most common measure uses the Euclidean distance [43, 17]. It represents the
straight line distance between two feature points in the Euclidean space.
However, the Euclidean distance is susceptible to different feature scales in
the vector. The Mahalanobis distance eliminates some limitations of the
Euclidean metric, such as automatically considering the scaling of the axes,
correcting for correlations between different features, and providing curved
and linear decision boundaries [42]. The Mahalanobis distance calculates the
covariance distance between two data points. Pearson correlation is widely
used to measure the degree of linear correlation between two variables [18,
44, 44]. However, in the above methods, a few outlier data in the training set
can significantly affect the classification results because any similarity-
based approach only needs to store a small number of training samples. Hence,
it is challenging to optimize, develop, or transform the training data
structure to improve classification performance.

The following eight machine learning-based methods are often used for user
authentication based on PPG signals: linear discriminant analysis [60],
support vector machine [71, 55, 83, 78, 81, 82, 79], k-nearest neighbor [19,
71, 80], random forest [83, 75, 69, 84], gradient boosted trees [78, 54, 55],
multi layer perceptron [82, 72], Gaussian mixture model [80], restricted
Boltzman machines [85]. They usually need to be trained using the features
from the feature capture layer as input and the class of the user as output.

In the end-to-end deep learning approach, convolutional neural networks (CNN)
is one of the most commonly used method and has a wide range of applications
in computer vision-related tasks [86, 87]. Recently, CNN has promising
applications in user authentication based on PPG signals [76]. CNN usually
consists of a convolutional layer, a pooling layer, and a fully connected
layer. The target’s low-level (points in the signal) and high-level features
(overall trend of the signal) can be extracted by stacking the convolutional
layers. Pooling layers are sampled to reduce the feature space and retain the
important features. The primary role of the fully connected layer is to
classify the signal based on the features previously extracted from the
convolutional and pooling layers.

In CNN, the signal from each layer of neurons can only propagate up one layer,
and the samples are processed independently of each other at each moment.
However, the PPG signals are time-series data, and the information of time
dimension is valuable. Therefore, some researchers have added LSTM after CNN
to capture long time contextual information [77, 88, 89, 9, 90]. LSTM is a
variant of Recurrent Neural Network (RNN). It adds a gate mechanism and a
memory unit to RNN to capture the long-term dependence of the input sequence
by recording information from different time periods. It also solves the
gradient disappearance and gradient explosion problems in RNN. Many solutions
like the transformer model [91] learn from sequence data. The current research
on deep learning models in PPG-based authentication is limited and requires
further exploration.

Biometric systems based on a single PPG signal are vulnerable since the
acquisition equipment and recording environment have a significant impact on
the performance of the system. A PPG signal collected with a precise sensor in
a controlled environment is reliable. However, if the PPG signal is unstable,
it should be combined with another biometric signal [92]. ECG can be recorded
simultaneously with PPG and provide a multi-fact biometric system. The sensor
can acquires the ECG and PPG signals simultaneously, thus synchronizing the
ECG and PPG values. The systolic peak of PPG and the R-peak of ECG can be used
to obtain Pulse Transit Time and Pulse Arrival Time to match user template,
determining whether it is a spoofing signal [93]. In order to bypass the
proposed anti-spoofing system, attackers must measure the victim’s ECG and PPG
at the same time. Even if the attacker generated the victim’s ECG and PPG,
matching them from the same time domain would not be possible. However, ECG
signals require the user to use additional measurement equipment, increasing
the system’s complexity. Ultra-wideband radar can measure the user’s breathing
pattern, and synchronization with PPG signals, which can be used to detect
unknown presentation attacks [94].

Current face recognition systems are vulnerable to spoofing attacks [95].
Although it is possible to distinguish malicious attackers by capturing local
details of real faces from fake ones, they may fail if highly realistic 3D
masks are used. Because PPG signals are present only in real living tissue and
not in any mask or printed material, facial liveness can be detected by
finding PPG signals in facial videos [96]. Since the colour value of facial
areas in the video varies with the heart pulse, the remote
photoplethysmoGraphy (rPPG) signal can be obtained from the face. The peak
amplitude of the rPPG spectrum could reflect the heartbeat intensity. Because
the rPPG signals are sensitive to illumination conditions and camera settings,
the observed amplitude is susceptible to environmental noise. Moreover, the
noise may dominate the observed signal. Cross-correlation operations of local
rPPG signals at different face regions to amplify the shared heartbeat
frequency can suppress the interference of non-periodic noise [97].

As the face image and video generation technology based on GAN becomes
gradually mature and realistic, anyone can now forge a face to replace the
face in the video [98]. This replacement technology is known as DeepFake [99].
DeepFake poses a real threat to the accuracy of the multimedia information
available. For example, falsifying a politician’s speech may lead to harmful
results. The live detection for face recognition mainly relies on detecting
heart rate, while heart rate may still be present in a DeepFake video, just
with a different pattern than the real one. One can detect DeepFake by
determining whether the normal heart rate in the face video is diminished
[100].

Reference | Source | Assessment | Permanence | Privacy | Revocability | Wearability | Continuity | Transparency | Accessibility
---|---|---|---|---|---|---|---|---|---
[90] | S | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[63] | S | Data-driven | $\CIRCLE$ | $\CIRCLE$ |  |  |  |  |
[64] | S | Data-driven | $\CIRCLE$ | $\CIRCLE$ |  |  |  |  |
[72] | C | Data-driven | - | $\CIRCLE$ |  |  |  |  | $\checkmark$
[71] | C | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[9] | S | Data-driven | $\CIRCLE$ | $\CIRCLE$ |  |  |  |  |
[55] | S | Data-driven | - | $\CIRCLE$ |  | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$
[101] | S | Data-driven | $\LEFTcircle$ | $\CIRCLE$ |  | $\checkmark$ |  | $\checkmark$ | $\checkmark$
[83] | C | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[84] | S | Data-driven | $\LEFTcircle$ | $\CIRCLE$ | $\checkmark$ | $\checkmark$ |  |  | $\checkmark$
[78] | S | Data-driven | $\LEFTcircle$ | $\LEFTcircle$ |  |  |  |  | $\checkmark$
[89] | C | Data-driven | - | $\CIRCLE$ |  | $\checkmark$ |  | $\checkmark$ | $\checkmark$
[88] | C | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[57] | S | Data-driven | $\CIRCLE$ | $\LEFTcircle$ |  |  |  |  | $\checkmark$
[75] | S | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[70] | C | Data-driven | - | $\CIRCLE$ |  | $\checkmark$ |  | $\checkmark$ | $\checkmark$
[79] | S | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[77] | S | Data-driven | - | $\CIRCLE$ |  | $\checkmark$ |  | $\checkmark$ | $\checkmark$
[76] | C | Data-driven | - | $\CIRCLE$ |  |  |  |  | $\checkmark$
[65] | S | Data-driven | $\LEFTcircle$ | $\CIRCLE$ |  |  |  |  |
[54] | S | Data-driven | - | $\CIRCLE$ |  | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$
[61] | C | Data-driven | $\LEFTcircle$ | $\Circle$ |  |  |  | $\checkmark$ | $\checkmark$
[82] | S | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[62] | S | Statistics-based | $\LEFTcircle$ | $\CIRCLE$ |  |  |  |  |
[69] | C | Data-driven | - | $\CIRCLE$ |  | $\checkmark$ |  |  | $\checkmark$
[80] | S | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[85] | S | Data-driven | - | $\CIRCLE$ |  | $\checkmark$ |  |  | $\checkmark$
[60] | S | Data-driven | $\CIRCLE$ | $\CIRCLE$ |  |  |  |  |
[43] | C | Statistics-based | $\Circle$ | $\LEFTcircle$ |  |  |  |  | $\checkmark$
[68] | S | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[19] | S | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[66] | S | Statistics-based | - | $\CIRCLE$ |  |  | $\checkmark$ |  |
[42] | S | Statistics-based | - | $\CIRCLE$ |  |  |  |  |
[92] | S | Data-driven | - | $\CIRCLE$ |  |  |  |  |
[18] | S | Statistics-based | - | $\CIRCLE$ |  |  |  |  |
[17] | S | Statistics-based | - | $\CIRCLE$ |  |  |  |  | $\checkmark$
TABLE I: Outline of reviewed papers attributes on user authentication.
“$\checkmark$”: Will work. “$\CIRCLE$”: High level. “$\LEFTcircle$”: Medium
level. “$\Circle$”: Low level. Permanence: The robustness of the
authentication to temporal changes, including long time intervals and mood
changes. Privacy: The potential exposure level of biometric signals, High
privacy level indicates low exposure level. Revocability: Whether the
authentication template can be revoked/replaced. Transparency: If the user can
perceive the authentication process. Accessibility: Whether it is suitable for
all populations, especially for people with physical disabilities. S: Single
pulse. C: Continuous waveform. “-”: Not consider

###  III-D Summary

The five aspects of PPG-based user authentication are summarized below.

Revocability: Biometric systems usually require biometrics to be permanent.
However, once the biometric template is exposed, the threat to the
identification system is permanent. Revocability means that the template can
be replaced in biometric template exposure. The most straightforward revocable
authentication is to encrypt the biometrics in the device. In PPG-based user
authentication, feature transformations are used to map features into
different vector spaces to cancel templates.

Wearability: With the miniaturization of physiological signal sensors, most
wearable devices have these sensors built-in for healthcare. For wearable
authentication, PPG signals are primarily collected by wristband devices.
These wristband devices are usually inexpensive. In addition to the normal
acquisition of the signal, gestures can also be used to assist in
authentication by changing the signal shape.

Continuity: Currently, in most authentication scenarios, authentication is
usually performed only on the first access. The user identity is maintained by
the credentials obtained through authentication. It may lead to security risks
for subsequent operations. Continuous authentication enables continuous
verification of the user’s identity for the entire duration of the session.
While traditional continuous authentication methods typically rely on
transient events, PPG signals are continuous waveforms that can easily provide
non-intrusive continuous authentication.

Transparency: Transparent authentication is imperceptible to the user. It does
not require tedious user interactions and can enhance the user experience.
Wearable device based PPG user authentication offers the possibility of
transparent user authentication. It reduces the probability of a spoofing
attack since the user does not know when the authentication occurred.

Accessibility: It represents the applicability to different groups of people,
for example, whether some people with physical disabilities can use it. Many
popular authentication methods have accessibility barriers, e.g., the
fingerprint of a burned finger. PPG signals can be collected in different body
parts (ears, forehead, fingers, toes), implying high accessibility.

Table I summarizes the concept of the user authentication-related articles we
reviewed.

##  IV Other Authentication Scenarios

With the development of mobile health (M-Health), more and more biomedical
sensors will be worn or implanted in individuals for disease monitoring,
diagnosis, and treatment [102]. To facilitate the joint processing of
spatiotemporal physiological information from different body parts, these
biomedical sensors often need to be linked using wireless communication
technologies. We refer to the networks composed of such links as Body Area
Sensor Network (BASN) [103]. Since the data transmitted in the network
contains personal and sensitive medical data, it is always essential to ensure
the security of the transmission. For wireless networks, the information in
transit is vulnerable to eavesdropping, spoofing, disruption, and traffic
analysis [104, 105, 106]. Current mainstream security schemes include
cryptography, steganography, and physical layer security access [107]. Unlike
traditional wireless sensors, biosensors have the following restrictions [53]:

  1. 1.

Low Power: Biosensor is vulnerable to the insufficient power supply. Even for
rechargeable energy, the tissue around the biosensor will be heated during the
charging process. This can lead to damage to human tissue, limiting the extent
to which biosensors can be recharged.

  2. 2.

Limited Memory: The amount of memory available for biosensors is severely
limited due to size and energy consumption limitations.

  3. 3.

Low Computation Capability: Their computing power is limited by lack of power
and memory. They cannot perform a large number of bit calculations due to a
lack of sufficient memory.

  4. 4.

Low Communication Rate: Communication is the most energy-consuming operation,
so it is important to keep traffic to a minimum.

Asymmetric encryption is used in traditional networks like local area network
and wide area network. However, asymmetric encryption is not ideal for BASN
because of its high demand in computational resource utilization, power
consumption, and memory size [108]. Hence, symmetric cryptography is widely
used in BASN. To explore the potential of PPG signals in ensuring M-Health
safety, we reviewed all the research on other authentication scenarios related
to PPG signals. This section elaborates the application of PPG-based methods
to encryption. Table II lists the technical information and performance of our
reviewed papers.

###  IV-A Generate the Entity Identifier

To solve the security problems in a BASN, a lightweight and resource-efficient
security solution based on biometrics is proposed in [53]. The entity
identifier (EI) of each node is generated using the physiological
characteristics of the human body to identify the node and protect the
transmission key. Specifically, symmetric keys are masked with random numbers
derived from the biological information of different body parts [53]. As shown
in Fig. 4, the human physiological signals are used to generate EI for each
node to identify the nodes and hide or unhide the keys. Since the PPG signal
is measured in many parts of a human body, the signal can be used to generate
EI to ensure the security of key distribution without complex key agreement
and key exchange protocols [20]. For example, the peak-to-peak interval of the
PPG signal can be used as the EI. The EIs generated in the same BASN are
similar, while significant differences exist in different BASNs [21]. Inter-
pulse interval (IPI) and heart rate variability (HRV) are suitable
physiological parameters to generate EIs [46, 22, 20].

A NIST standards test was used for the randomness assessment, including a
frequency (monobit) test, an intra-block frequency test, a cumulative sums
test, a run test, and a discrete Fourier transform test. The above methods
used to generate the EI showed an acceptable random level by passing the NIST
test.

Biometric features are time-dependent and should be captured synchronously on
different devices of the same BASN. However, individual IPIs may be
desynchronized as a result of the cardiovascular pulse timing delay and signal
transmission delay. An error tolerance mechanism can reduce the impact of this
discrepancy, allowing us to use fewer IPIs to generate EIs of the same bit
length while producing better false accept rates and false reject rates [45].

However, both HRV and IPI used in the above methods need R-wave detection
before they are obtained. In addition, it takes much time to generate multiple
HRV and IPI to ensure the system’s security. Therefore, researchers began to
explore methods based on PPG frequency domain features. Fast Fourier transform
can be used to obtain the frequency domain characteristics of PPG signals
[109]. Only five seconds of measurement is enough to generate a usable EI. The
values of time-domain components in different body parts (such as two fingers)
have similar trends, but differences exist in the values. In contrast, the
frequency components of PPG signals measured from any part of the body had
similar values at any given time. Compared with the IPI-based scheme, the Fast
Fourier Transform-based EI generation scheme is less sensitive to signal
quality. However, the randomness and identification performance of the
frequency domain features is poor compared to the components in the time
domain. The use of energy distribution information based on physiological
signals can overcome this problem [110]. The PPG signal generated by two
sensors of the same person, despite differing in the time domain of signal
shape. It usually has similar characteristics in energy distribution.

Figure 5: A fuzzy vault scheme. The sender encodes the generated key using an error correction code. Then, the encoded information is used to construct a polynomial. Subsequently, the captured PPG signals are mapped into the polynomial to obtain the point set $A$, and a random point set $B$ is generated whose elements are not in $A$. $A$ and $B$ together form the vault. Eventually, the receiver uses the synchronously collected PPG signals to reconstruct the polynomial and recover the key from the vault. The asterisk symbol ‘*’ represents the solution of the recipient in the cloud. The remote service cannot obtain the user’s PPG signal synchronously, which is accomplished by deploying the generation model of the PPG signal in advance. Reference | Effectiveness | Error Correction Method | Location | Randomness | Low Energy Consumption | Low Memory Overhead
---|---|---|---|---|---|---
[111] | $\CIRCLE$ | Information reconciliation | Local | $\CIRCLE$ | - | $\CIRCLE$
[47] | $\LEFTcircle$ | Regression analysis | Local | $\CIRCLE$ | $\CIRCLE$ | $\CIRCLE$
[48] | $\LEFTcircle$ | Fuzzy-vault scheme | Local | $\CIRCLE$ | $\LEFTcircle$ | $\LEFTcircle$
[49] | $\LEFTcircle$ | Fuzzy-vault scheme | Local/Cloud | $\LEFTcircle$ | $\LEFTcircle$ | $\Circle$
[50] | $\CIRCLE$ | Fuzzy-vault scheme | Local | $\CIRCLE$ | $\LEFTcircle$ | $\Circle$
[51] | $\CIRCLE$ | Cluster | Local | $\CIRCLE$ | - | -
[52] | $\LEFTcircle$ | Fuzzy-vault scheme | Local | $\LEFTcircle$ | $\Circle$ | $\Circle$
[46] | $\CIRCLE$ | - | Local/Cloud | $\CIRCLE$ | - | $\CIRCLE$
[45] | $\CIRCLE$ | Fuzzy commitment scheme | Local | $\CIRCLE$ | - | $\CIRCLE$
[22] | $\CIRCLE$ | Fuzzy commitment scheme | Local | $\CIRCLE$ | - | $\CIRCLE$
[21] | $\CIRCLE$ | - | Local | $\CIRCLE$ | - | $\CIRCLE$
[20] | $\CIRCLE$ | Fuzzy commitment scheme | Local | $\CIRCLE$ | - | $\CIRCLE$
TABLE II: Outline of reviewed papers on PPG-based encryption scheme.
Effectiveness: Time required for entity identifier generation. “-”: Not
measured or used. “$\CIRCLE$”: High level. “$\LEFTcircle$”: Medium level.
“$\Circle$”: Low level. Location: Location of encryption scheme deployment.
PPG-based encryption scheme can be deployed on a local BASN, or a remote cloud
server.

###  IV-B Key Distribution Solution

In the traditional symmetric encryption scheme, both sides of the
communication must share the key through the key distribution scheme before
any encryption process. Existing key distribution technologies such as random
key pre-distribution protocol and key distribution based on polynomial pool
[112], require some forms of pre-deployment. As BASN deployments increase,
these methods may involve considerable latency, as they require pre-
deployment. Moreover, whenever a body sensor needs to be added or changed, a
new key is also required to be deployed to guarantee that the new device can
communicate securely with the existing sensor. Therefore, BASN needs a novel
key distribution solutions that do not require any pre-deployment to provide
timely security.

A physiological value-based (PV) security scheme uses biometric features (like
IPI) from the deployment environment (human body) to perform an XOR operation
with the key for key obfuscation [51]. The receiver uses its own version of
the PV to unmask the key and then verifies or decrypts the received data. The
use of physiological signals potentially avoids any additional explicit key
distribution after network establishment. Moreover, the keys are dispatched
during message transmission, thus avoiding any extra steps of communication
and increasing the energy efficiency of the BASN.

However, PV measured on two different body parts often yielded slightly
different values. One way to overcome the differences between PV is to
consider them as errors introduced during data transfer between communicating
sensors [53]. Therefore, a simple error correction function can be used to
mitigate the problem of slightly different PV between the sender and receiver.

Moreover, two important property of PVs is time-varying and change
unpredictability. It prevents malicious intent to predict the presence of PV
by past PV. However, this time variation also poses a time synchronization
problem for the sender and receiver. Fuzzy commitment borrows from the bit
commitment scheme in cryptography. Although a certain number of errors in the
signal features are allowed in the fuzzy commitment scheme, it does not allow
the disorder of these symbols. To address this problem, the frequency features
of the PPG signal were used in [52] as PV and fuzzy vault distribution scheme
[113]. The PV is obtained by fast Fourier change on the acquired PPG signals.
Fuzzy vault scheme to compensate for the difference between the feature
vectors of the sender and receiver. Fig. 5 shows the detailed flow of the
fuzzy vault scheme. It is an improved extension to the fault tolerance level
of the fuzzy commitment scheme. The sender hides the key $k$ in a vault. The
receiver uses a private key derived from his/her PPG signals to decrypt $k$ in
the vault, assuming that the receiver’s features are similar enough to the
sender’s features and enough elements overlap.

The existing fuzzy vault schemes rely on the error correction codes that only
start to take effect after the polynomial coefficients have been reconstructed
by the receiver, which barely improve the tolerance of biometric identifiers
[50]. Error correction coding can be used on actual points (physiological
feature points) to ensure the redundancy of biometric identifiers. The
security strength of a traditional fuzzy vault scheme correlates to the
vault’s size. The more chaff points, the harder it is to destroy the vault.
However, increased vault size will cause a conflict between the features
generated by different sensors, resulting in an error rejection. By ranking
physiological features, the probability of conflict can be reduced [48].
Regression analysis can save at least 90% of the power consumption of the
sensors compared to the fuzzy vaulting scheme [47]. Zhang et al. [111] indexed
each IPI with sequence numbers to synchronize time differences and use the
information reconciliation to correct key mismatches between the sender and
the receiver.

The previous approaches aim to protect the authentication system between human
sensors in M-health. Unfortunately, it is necessary to protect the
authentication system between the sensors and the cloud in practical
applications because the cloud cannot directly verify the user’s physiological
signals. Usually, the data acquired by the sensor is initially transmitted to
a base station, a smartphone, or a proprietary dongle before being transmitted
to the cloud via an asymmetric encryption scheme. All communication links are
potentially vulnerable. Establishing an end-to-end communication channel
between the sensor and the cloud will minimize the loss of sensitive medical
data, even if the intermediate communication channel or node is compromised
[49]. In particular, the sender obtains the frequency domain characteristics
of the PPG signals by the Fourier transform. Then the fuzzy vault scheme is
used to distribute the hidden key. A generation model is used at the receiver
side to learn the morphological parameters of the user’s PPG signals.

###  IV-C Summary

In other authentication scenarios, PPG signals is mainly used in the context
of BASN. Sensors or nodes in a BASN need to identify whether they belong to
the same person to avoid eavesdropping or tampering. The devices in a BASN
have strict memory and energy constraints due to their sizes, which greatly
limits the use of key distribution schemes. Medical sensors deployed in
various body parts can authenticate messages by capturing PPG signals as
biometrics. This setup avoids the overhead of key pre-distribution in
traditional message authentication schemes.

##  V Research Gap and Future Work

We have found that many studies have successfully applied PPG signals to
authentication through an extensive literature survey. The PPG signal has
unparalleled advantages over traditional biometric features. However, research
on PPG-based authentication is in its infancy. To help future research, we
discuss the current challenges and future research directions.

###  V-A Challenges in User Authentication

The first challenge for user authentication is signal quality. Since PPG is a
physiological signal, it is subject to constant changes under various factors.
This variation may trigger potential vulnerabilities in the authentication.
Therefore, it is important to study the persistence and robustness to changes
of PPG signals in massage authentication applications. It also poses a
significant challenge to signal quality. The quality of PPG signals is
affected by the following two aspects:

The influence of intrinsic factors: PPG acts as a physiological signal that
changes over time. Most existing studies investigate the single session
(continuous signal measurements during the same period). In practical
applications, many scenarios are cross-session (Enrollment and authentication
phases in different sessions) [9, 88, 78, 64, 62]. The performance of cross-
sessions in their results is worse than that of single sessions. It suggests
that the current approach is not robust to the change of PPG signals as time
varies. In addition, the effect of emotion on the PPG signals is also
significant. The influence of emotions in certain situations can help us
resist illegal certifications like enforcing a convict to authenticate. When
we are anxious to authenticate, the influence of emotions is negative. In
studies of the effect of emotion on PPG signals, video or game is currently
used to stimulate participants’ emotions. This method consists of many
uncontrollable parameters. Through the participants’ descriptions, we cannot
objectively determine the true emotions because it leads to a large amount of
possible misinformation in the data. For intrinsic influences, we can explore
features that are insensitive to time and mood changes. Although the deep
learning-based approach eliminates the need for manual feature extraction and
automatically extracts the information needed for the task from the PPG
signal, it requires a large amount of training data.

The influence of external factors: The PPG signal is collected by the optical
principle. The external lighting conditions affect the signal quality.
Wearable devices have become increasingly popular for capturing PPG signals.
Thus, motion artifact noise caused by movement may significantly impact the
cardiac signal, threatening the feasibility of processing solutions. While
filtering noise is an option, it can also alter the timing information of the
PPG signals during a cardiac cycle. Moreover, skin temperature and skin tone
will also affect the quality of the PPG signal.

The second challenge comes from the dataset. Table III presents a comparative
analysis of publicly available datasets in different dimensions. It is
challenging to collect an extensive data set in different states (movement
status and emotions) as a physiological signal. The current data set with the
most extensive data with different states has only 170 participant signals.
Moreover, the interval between their measurements was only 18 days. In
existing datasets, the resting state PPG signals are mainly considered. The
controlled environment in the experiment is far from our daily life,
indicating that the signal noise in the data is significantly less than that
in the real-world application. To solve the above challenges, we should first
construct a large dataset of PPG signals. In addition to recording the PPG
signals in different states in the data set, the controlled experimental
environment should be replaced with the daily life setting to simulate the
actual scenario.

The third challenge is the overhead of the device, especially in continuous
authentication. Continuous authentication requires sensors to continuously
monitor the user’s physiological signals, which requires additional
computational resources and energy overhead. These overheads are important
issues in resource-limited wearable devices and smartphones. In the future,
intelligent power management can be introduced in the certification process.
For example, we wake up the authentication system only when sensitive
operations are performed.

Moreover, data leakage is a big challenge. Although PPG signals are not easily
leaked, they will threaten the security of the authentication system once the
leak occurs. Furthermore, the development of radar, and remote PPG to collect
heart rate information makes it impossible to ignore the potentially severe
consequences of data leakage. Current research about revocability focuses on
the cancelable template. When a user template is compromised, it is replaced
by redeploying a new one. However, it does not consider when the raw signal is
leaked. In addition, the wearability and transparency of authentication
require the support of wearable devices. For stolen wearable devices, all the
authentication system components will be exposed to the adversary. One
solution is to restrict access to system components and block illegal access
when it occurs.

Most existing work investigates medical-grade devices. With the popularity of
wearable devices and the development of video technology, we believe that the
PPG signal-based security technology will be further developed in the future.
Other physiological signals are also receiving increasing attention as
promising candidates for implicit authentication systems [114, 115]. Some
recent publications introduce biometric applications, including brain
biometrics for consumer devices [116], ECG signals for user authentication
systems [117], and electrical muscle stimulation for individual authentication
[118].

Reference | Subjects | Location | Time Span | Status | Storage | Acquisition Environment | Health Status
---|---|---|---|---|---|---|---
TBME [119] | 42 | FT | $\Circle$ | Patient | Electrical signals | Clinical | $X$
Biosec3 [64] | 170 | FT | $\CIRCLE$ | Exercise | Electrical signals | Office | $\checkmark$
Schrumpf et al. [120] | 50 | F | $\Circle$ | Patient | Video | Clinical | $X$
SeeingRed [78] | 15 | FT | $\CIRCLE$ | Relax | Video | Laboratory | -
PPG-ACC [121] | 7 | W | $\Circle$ | Exercise | Electrical signals | Laboratory | $\checkmark$
VIPL-HR [122] | 42 | F | $\Circle$ | Exercise | Video | Laboratory | $-$
UBFC-RPPG [123] | 50 | F | $\Circle$ | Exercise | Video | Laboratory | $-$
TokyoTech [124] | 9 | F | $\Circle$ | Relax | Video | Laboratory | $-$
CIME-PPG [125, 126] | 48 | FT | $\Circle$ | Exercise | Electrical signals | Laboratory | $X$
PPG-DaLiA [127] | 15 | W | $\CIRCLE$ | Exercise | Electrical signals | Wild | $\checkmark$
OBF [128] | 106 | F | $\Circle$ | Exercise | Video | Laboratory | $X$
LGI-PPGI [129] | 25 | F | $\Circle$ | Exercise | Video | Wild | $-$
GYRO-ACC [130] | 24 | W | $\Circle$ | Exercise | Electrical signals | Laboratory | $\checkmark$
BloodLosSVM [130] | 36 | FH/FT/E | $\Circle$ | Patient | Electrical signals | Clinical | $X$
WESAD [131] | 15 | W | $\Circle$ | Emotion | Electrical signals | Laboratory | $\checkmark$
PPG-BP [132] | 219 | FT | $\Circle$ | Relax | Electrical signals | Clinical | $X$
PulseID [76] | 43 | FT | $\Circle$ | Relax | Electrical signals | Office | $\checkmark$
Biosec1 [65] | 41 | FT | $\CIRCLE$ | Exercise | Electrical signals | Office | $\checkmark$
COHFACE [133] | 40 | F | $\Circle$ | Relax | Video | Laboratory | $\checkmark$
Jarchi et al. [134] | 8 | W | $\Circle$ | Exercise | Electrical signals | Laboratory | $-$
Vortal [135] | 57 | FT/E | $\Circle$ | Relax | Electrical signals | Laboratory | $\checkmark$
MIMIC-III [136, 137] | 10,282 | FT | $\CIRCLE$ | Patient | Electrical signals | Clinical | $X$
BIDMC [138, 137] | 53 | FT | $\CIRCLE$ | Patient | Electrical signals | Clinical | $X$
TROIKA [139] | 20 | W | $\Circle$ | Exercise | Electrical signals | Laboratory | $-$
PURE [139] | 10 | F | $\Circle$ | Exercise | Video | Laboratory | $-$
DEAP [140] | 32 | FT | $\Circle$ | Emotion | Electrical signals | Laboratory | $\checkmark$
TABLE III: Comparison of publicly available PPG datasets in different
dimensions. Subjects: Number of participants in the dataset. Location: Which
body part the signal was collected. F: Face. FT: Fingertip. W: Wrist. E: Ear.
FH: Forehead. “$\CIRCLE$”: Cross-session. “$\Circle$”: Single-session.
Patient: The participant is under medical supervision. Relax: Participants
remain as stationary as possible during signal acquisition. Exercise:
Including Running, Cycling, Walking, Climbing. Emotion: Use games or videos to
stimulate participants’ emotions. “-”: The health status of the participants
was not considered. “$X$”: There were health problems among the participants.
“$\checkmark$”: All participants are healthy.

###  V-B Challenges in Other Authentication Scenarios

Regarding other authentication scenarios, one of the big challenges in using
PPG signals for encryption purposes is the speed of EI generation. The length
of EI generated using PPG signals is usually proportional to the security. EI
generation based on time-domain features takes about 30 seconds of signal to
generate 128-bit EI, significantly increasing communication latency. Although
it can generate EI of sufficient length faster in the frequency domain-based
features, it is not as good as the time-domain features in terms of
randomness. Randomness is also one of the indicators to assess the system’s
security. Sufficient randomness will make it difficult for an attacker to
guess and compromise the system’s security. Producing EI to guarantee speed
and randomness is a direction worthy of further research.

Regarding the key distribution scheme, it is challenging to make the most
efficient use of the strictly limited memory to ensure communication security.
The fuzzy vault scheme requires many chaff points to obfuscate the true signal
signature. Increasing the number of chaff points will increase the vault’s
security and increase memory consumption. A simple solution is not to pass the
key information in each message transmission but to update the key information
at certain intervals, significantly reducing the memory consumption problem.

In addition to its application in BAN communication, PPG signals have a
broader application prospect. For instance, the zero-trust architecture is
gradually coming into public view with global digitization as a new technical
architecture for cybersecurity [141]. It considers that the network is in a
dangerous environment all the time. Thus, all devices, users, and network
traffic should be authenticated and authorized before communication. In a
zero-trust setup, identity will be used as the basis of access control. As a
physiological signal that everyone has, the PPG signal does not need to be set
up in advance. It can naturally be used as a signal to verify a person’s
identity.

###  V-C Attack Threats

Although it is challenging to steal unobservable PPG signals, PPG-based
authentication faces potential threats. Three main types of attack threats
exist: analyzing the weak PPG signal from the face by HD cameras, collecting
heart rate information via Ultra-wideband radar and stealing user templates
through adversarial machine learning.

With the development of biomedicine, more and more studies have shown that
contactless methods can be used to detect heartbeat signals [142, 143]. Among
them, HD cameras-collected rPPG signal brings a serious threat to the PPG-
based security system because of its easy-to-acquire and long-distance-use
characteristics. The rPPG signal can acquire 70% of IPI information obtained
by the contact sensor [144]. When using rPPG to estimate IPI, darker skin has
a higher average bit error rate and is more difficult to detect IPI
accurately. This is due to the higher melanin content in darker skin compared
to lighter skin, which reduces the diffuse reflection containing pulsation
information, thus reducing signal quality. Light sources with a poor balance
of radiated energy in the RGB channel can reduce detection accuracy. The head
rotation also affects the accuracy of rPPG because it changes the light
reflected from the skin. In addition, compression of the video causes signal
artifacts that can lead to false detection of heartbeats, but it does not
significantly affect the detection of IPI.

The above research reveals the potential threats of IPI-based key distribution
schemes. Although rPPG has been successfully applied to detect 3D mask
presentation attacks and DeepFake videos, it is susceptible to environmental
noise due to the particularly weak signal it obtains. At present, it is mainly
used to obtain simple time- and frequency-domain features such as HRV and IPI
to attack the corresponding security systems. In real-world scenarios, the
victim’s environment may constantly be changing. The camera is susceptible to
the user’s background environment.

Another non-contact method of detecting heartbeat signals is based on the
Ultra-wideband radar. It measures the heartbeat by the variation in the
amplitude and the arrival time of the reflected pulses. PPG is an optical
signal that cannot be detected directly by radar. This setup allows radar-
based methods to detect only heart rate information such as HRV and IPI of the
heartbeat. Therefore, it is used the same way as the HD camera approach,
mainly for attacking systems based on simple features such as HRV and IPI. It
does not mean that the HD camera- and radar-based approach is not a threat.
There is already research to obtain high-quality rPPG signals using generative
models [30]. Furthermore, radar information for reconstructing the ground
truth PPG signal is also a possible threat. Yamamoto et al. [145] explored the
potential to reconstruct the ECG signal using the information collected by the
doppler sensor. However, no researcher has used it for the reconstruction of
PPG signals.

With the popularity of machine learning methods in PPG-based authentication,
adversarial machine learning is becoming a promising attack method.
Adversarial machine learning misleads the model by adversarial examples,
producing the wrong output. PPG signals can be simulated using dynamic models.
Gaussian functions can be applied to construct the mapping function that
converted the attacker’s PPG signals into dynamic model parameters similar to
those of the victim to deceive the biometric system. We also refer to this
attack method as a gray-box evasion attack. It needs to obtain the victim’s
PPG signal in advance, making this method very limited. To the best of our
knowledge, there are no defenses against PPG-based authentication adversarial
examples. Traditional adversarial defenses are usually divided into two
categories, detecting adversarial examples and improving the robustness of the
classifier to adversarial examples (e.g., adversarial retraining and
distillation). However, even with the state-of-the-art defense approach, there
are still effective methods of attack [146]. In addition, other attack methods
against machine learning have tremendous potential [147, 148]. [147] spoofs
biometric systems by generating adversarial examples. [148] uses the update
process of unsupervised templates to perform poisoning attacks on the model.
Each of these approaches is a potential threat to machine learning-based
biometric systems.

##  VI Conclusion

Traditional biometric authentication is susceptible to the threat of
presentation attacks. Physiological signal-based authentication has recently
received much attention, especially PPG signals. This survey presents a view
of PPG-based authentication. Specifically, three aspects, signal extraction,
signal noise reduction, and feature conversion and selection, reveal the pre-
processing work needed before applying PPG signals. Then we described how they
work in the authentication scenario. Regarding user authentication, the
literature is divided into three main categories, pattern recognition, resist
noise, and resist attacks. The literature related to other authentication
scenarios is divided into two main categories, entity identifier generation
and key distribution. Based on the existing research review, the challenges
are identified, and future directions are proposed to match the various
limitations. In addition, the attack threats against PPG-based authentication
are summarized. Thus, this survey can help researchers understand PPG signal-
based applications’ current development in security and future research
trends. Most studies in this review were conducted within the last few years,
indicating a fast-growing interest in applying PPG signals among researchers
in the security community. It shows the broad potential of biomedical signals
such as PPG signals for authentication.

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