# A Region-Based Descriptor Network For Uniformly Sampled KeyPoints

###### Abstract

Matching keypoint pairs of different images is a basic task of computer
vision. Most methods require customized extremum point schemes to obtain the
coordinates of feature points with high confidence, which often need complex
algorithmic design or a network with higher training difficulty and also
ignore the possibility that flat regions can be used as candidate regions of
matching points. In this paper, we design a region-based descriptor by
combining the context features of a deep network. The new descriptor can give
a robust representation of a point even in flat regions. By the new
descriptor, we can obtain more high confidence matching points without
extremum operation. The experimental results show that our proposed method
achieves a performance comparable to state-of-the-art.

Index Terms—  keypoint extraction, feature descriptors, uniform sampling, deep
learning

##  1 Introduction

Matching keypoint pairs between multiple images is an important problem to
Structure-from-Motion (SfM) [1], Simultaneous Localization and Mapping (SLAM),
camera pose estimation, image retrieval [2], 3D reconstruction [3]. The
general process to get sparse matching keypoint pairs is to first detect the
keypoints and then calculate their descriptors. Through some distance metric
of the descriptors such as the Euclidean distance and some feature matching
algorithm, such as the nearest neighbor search (NNS) algorithm [4], the
keypoints can be matched.

Fig. 1: According to common sense, there is no key point in flat regions, like
point C shown in Fig.1. The point C with a small neighbor (red circle) can not
give a valid description. With the increase of the receptive field, it is
possible to find a valid description for point C with a larger neighbor (green
circle).

For the hand-crafted method, the local feature extraction process is generally
detect-then-describe. Firstly, detect the coordinates of the keypoints uses
some custom extremum algorithm. And then according to some prior knowledge, a
high-dimensional vector describing the features around the keypoint is
designed. These keypoints are then matched by using these high-dimensional
vectors. The best known traditional algorithm is SIFT [5], which obtains the
position of the keypoints based on the extremum detection of the complicated
Difference of Gaussian. It continues to be improved. RootSIFT [6] enhances the
matching accuracy through normalized descriptors, [7] uses binary descriptors
to improve real-time performance. [8] proposes a quantization expression to
provide sparse representation for image blocks. However, such algorithms
usually only consider low-level information, such as corners [9] or blob-like
structures [5, 10]. Their results deteriorate under some challenging
conditions, e.g., the large variation of light intensity, sharp change of
viewpoint, or weak texture area [11]. And in these challenging scenarios, they
are easy to produce a lot of mismatched results, which increases the
difficulty of further processing. Moreover, they often need to design a
complex algorithm to complete the process of feature point detection and
description.

Fig. 2: The architecture of the proposed network. An image is fed to the
pipeline consisting of a feature extraction network (indicated by the blue
dotted box) for low-level information extraction around the point, and a high-
level feature fusion module (indicated by the red dotted box) for integrating
the high-level information and the low-level information. In order to extract
high-level information, we use a spatial pyramid pooling (indicated by the
green dotted box) module to obtain the information at different scales around
the point. Finally, the output of the descriptors is obtained through an L2
normalization layer.

Many learning-based approaches hope to obtain the coordinates or descriptors
or both of them of the keypoints with a data-driven approach. MatchNet [12] is
an early example. It uses a similarity measure network to calculate the
similarity of descriptors, which significantly improves previous results.
Later LIFT [13] is the first completely learning-based end-to-end network that
outputs both keypoints coordinates and descriptors. DELF [14] is applied to
the image retrieval task of street view markers, roughly locating the
keypoints on the image through an attention mechanism. SuperPoint [15] is
based on the synthetic datasets, which also outputs keypoints coordinates and
descriptors at the same time. D2-Net [16] obtains keypoints coordinates and
descriptors by selecting soft local-max in a pre-trained network. These
learning-based methods, which obtain the coordinates and descriptors at the
same time, generally adopt multi-task optimization to train the network, which
imperceptibly increases the training difficulty of the network. All of the
above methods need some custom extremum to obtain the feature points of the
texture region. Moreover, most of the methods focus on the local features of
keypoints and do not consider the high-level information around the keypoints.

According to common sense, there is no key point in flat regions, like points
C and D shown in Fig.1. For the points C and D with a small neighbor (red
circles) neither give valid descriptions. With the increase of the receptive
field, it is possible to find valid descriptions for points C and D with a
larger neighbor (green circles). Therefore, we propose a descriptor extraction
network that fuses more information which contains the features of the texture
area in the green circle (fusion of the high-level information and low-level
information around the keypoints), makes points C and D divisible, and ensures
that points in the flat region can also be well matched.

Our contributions can be summarized from two aspects:

1\. We introduce a simple uniform sampling method for keypoint extraction to
enlarge the candidate range of keypoints, which can obtain a larger
distribution of the matching feature point pairs accompanying by more abundant
image information.

2\. We propose a region-based descriptor network (RDN) that integrates high-
level information and low-level information around the points, which has
better expressive power and even makes the points in the flat region can be
distinguished from each other.

##  2 PROPOSED METHOD

###  2.1 Motivation and Our Ideas

In general, the coordinates and the descriptors of the keypoints need to be
obtained firstly for further matching. Most of the previous methods used
various extremum points as feature points, which are concentrated in the
region with rich texture. In this way, the points out of the rich texture
regions are usually not considered as candidates of a matching point. We hope
to integrate more comprehensive image information so that flat areas can also
have feature points to participate in matching.

####  2.1.1 Keypoints Extraction

In many tasks, the keypoint does not have a unified definition, and whether it
is an extreme point does not affect the result of matching, such as in the
calculation of the fundamental matrix [17]. As long as a sufficient number of
point pairs match correctly, we can calculate the relationship between the
images, such as the rotation and translation of the image perspective.
Therefore, we came up with a uniform sampling method on the image to extract
keypoints.

####  2.1.2 Learning Descriptors

As shown in Fig.1, the local features extracted by the previous methods cannot
well accomplish the matching of feature point pairs in flat regions, that is,
the descriptors of feature points in flat regions are indistinguishable. So we
propose a fusion of high-level information and low-level information
convolutional neural network to address this problem.

###  2.2 Network Architecture

The proposed network, referred to as RDN, aims to generate valid descriptors
for points of an input image even in flat regions. As shown in Fig.2, the
input is an image $I\in R^{H\times W\times 3}$. The front part of the RDN is
the feature extraction network (FEN) which is inspired by L2-Net [18] and R2D2
[19], and the latter part is a high-level feature fusion module (HFFM). The
128-dimensional vector generated by the FEN represents the low-level features
($d_{low}\in R^{H\times W\times 128}$) around the keypoints. Then it serves as
the input to the HFFM. The output of the HFFM is the high-level features
($d_{high}\in R^{H\times W\times 128}$) around the keypoints. To effectively
captures contextual relations around the keypoints, the HFFM starts with four
fixed-size average pooling blocks for Spatial Pyramid Pooling (SPP): $64\times
64,32\times 32,16\times 16$, and $8\times 8$. Then the feature maps are
upsampled to the same size as the input figure. By concatenating the outputs
of the FEN ($d_{low}$) and the HFFM ($d_{high}$), we can obtain a
256-dimensional vector, for each pixel. To better compare the descriptors, an
L2 normalization layer was added to normalize the descriptors. The final
output are the dense descriptors $d_{output}\in R^{H\times W\times 256}$.

###  2.3 Training Loss

In terms of descriptor learning, the descriptors are expected to be
distinctive and be avoiding mismatching. We adopt the triplet loss similar to
D2-Net, which has been successfully applied in descriptors learning [20]. The
RDN does not need to predict the coordinates of keypoints, so we remove the
weighted part of loss calculation in D2-Net, and directly calculate the
triplet loss between the distance of the descriptors of the matching keypoints
and the descriptors of the

Fig. 3: Feature points on flat areas of the table can also be matched as shown
in the black dotted box.

mismatched keypoints. It can be formulated as follows:

$Loss(I_{1},I_{2})=\sum_{c\in C}f(p(c),n(c)),$ (1)

where $I_{1}$, $I_{2}$ are two images with the same scene, f indicates the
triplet margin ranking loss, c denotes the correspondences, p represents the
distance between the descriptors of the matched keypoints pair
(correspondence), n is the nearest distance between the descriptors of the
mismatched keypoints pair, C is the set of the correspondences in the two
images. See [16] for more details.

Fig. 4: Matching results of different scenarios.

##  3 EXPERIMENTS

###  3.1 Dataset and Implementation Details

To generate training data close to the real scenario, we use the MegaDepth
Dataset [21] from the internet photo gallery. Since D2-Net has extracted the
pixel-wise correspondences with the COLMAP [1, 22], we directly use it as
ground truth to train our network.

The architecture similar to L2-Net, pretrained on the Oxford and Pairs
retrieval dataset [23] and the Aachen Day-Night dataset [24], was used to
initializes the FEN. The HFFM was fine-tuned for 50 epochs using Adam with an
initial learning rate $10^{-3}$, which was further divided by 2 every 10
epochs. 100 images were randomly selected from each scene for training to
solve the problem of the unbalanced number of images in the scene. The size of
the training batch is 1.

###  3.2 Qualitative and Visual Evaluation

To verify the effectiveness of our proposed method, we conduct some
experiments on pictures taken with a mobile phone. As we can see in Fig.3, not
only textured areas have many matching points, but flat areas, such as
tabletop, cabinet surface, and glass surface, can also get matching keypoints.

Our method is completed by referring to D2-Net, so we carry out some
experiments comparing with D2-Net. D2-Net and our method are used to get the
feature points and descriptors respectively in the experiment. The NNS
algorithm is used to match the feature points, and then RANSAC is used to
screen out the outliers. The final result is shown in Fig.4. We can see that
D2-Net matches basically the points in the texture region, and our method has
significantly more matching point pairs in the flat region than D2-Net. Our
method can preserve more information about the image and introduce less error
for subsequent advanced tasks.

###  3.3 Quantification and Camera Localization

Table 1: Results on the Aachen Day-Night dataset, we report the percentages of images that are successfully located under the 3 error thresholds. The best performance is shown in bold. Method | 0.5m,$2^{\circ}$ | 1m,$5^{\circ}$ | 5m,$10^{\circ}$
---|---|---|---
RootSIFT | 33.7 | 52.0 | 65.3
HAN+HN | 37.8 | 54.1 | 75.5
SuperPoint | 42.8 | 57.1 | 75.5
DELF | 39.8 | 61.2 | 85.7
D2-Net | 44.9 | 66.3 | 88.8
R2D2 | 45.9 | 67.3 | 88.8
RDN(ours) | 65.3 | 75.5 | 84.7

We evaluate our approach in a camera localization task. The dataset that we
use is the Aachen Day-Night dataset with dramatic changes in illumination.
This dataset has night-time images with unknown camera pose and day-time
images with a known camera pose. The day-time images need to be reconstructed
first to obtain the 3D scene structures, and then the night-time query images
should be located from these 3D structures. We follow the evaluation protocol
[24] and report the percentage of successful camera localization obtained from
night-time image queries within three error threshold
(0.5m,$2^{\circ}$)/(1m,$5^{\circ}$)/(5m,$10^{\circ}$). The camera pose error
consists of the camera position (meter) and the camera orientation (degree).
We compare the performance of the proposed approach with other methods
including RootSIFT, HardNet++ (HAN) with HesAffNet features (HN), SuperPoint,
DELF, D2-Net, R2D2. In addition to our method, the data in Table 1 directly
adopt the data of [19].

As we can see in Table 1, our approach achieves the best result under strict
threshold conditions for camera pose estimation. By means of uniform sampling,
our method retains more regional information, and meanwhile, by integrating
more context information, obtains a more comprehensive description of point
features and better express the information contained in the image. Therefore,
our method can achieve the most accurate camera positioning. The reason why
the effect is not good under the condition of a large error threshold is that
the results obtained by our method in the reconstructed the 3D model and
feature extraction of the night-time images mainly focus on the high-precision
part, while the parts with low-precision are relatively few, so the influence
of relaxing threshold is not as good as other methods. The results show that
our simple approach can be used for estimating accurate camera pose.

##  4 CONCLUSIONS

In this paper, we introduce a new method to obtain keypoints without extremum
operation, which significantly increases the range of keypoints acquisition.
This may lead to a problem that points in a flat region have no locally
meaningful features. To address this problem, we propose a region-based
descriptor by combining the context features of a deep network. We obtain more
expressive descriptors by combining high-level information and low-level
information. Compared to our baseline D2-Net, we obviously obtain keypoints
pairs of more regions that could express more information about the images.
Moreover, our approach achieves the best performance in terms of camera
positioning by collecting more information from the images under a strict
threshold.

## References

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# Implementation and Applications of WakeWords Integrated with Speaker
Recognition: A Case Study

Alexandre Costa Ferro Filho Computer Institute Goias Federal University
Goiânia, Brazil<EMAIL_ADDRESS>Elisa Ayumi Masasi de Oliveira
Computer Institute Goias Federal University
Goiânia, Brazil<EMAIL_ADDRESS>

Iago Alves Brito Computer Institute Goias Federal University
Goiânia, Brazil<EMAIL_ADDRESS>Pedro Martins Bittencourt Computer
Institute Goias Federal University
Goiânia, Brazil<EMAIL_ADDRESS>
###### Abstract

This paper explores the application of artificial intelligence techniques in
audio and voice processing, focusing on the integration of wake words and
speaker recognition for secure access in embedded systems. With the growing
prevalence of voice-activated devices such as Amazon Alexa, ensuring secure
and user-specific interactions has become paramount. Our study aims to enhance
the security framework of these systems by leveraging wake words for initial
activation and speaker recognition to validate user permissions. By
incorporating these AI-driven methodologies, we propose a robust solution that
restricts system usage to authorized individuals, thereby mitigating
unauthorized access risks. This research delves into the algorithms and
technologies underpinning wake word detection and speaker recognition,
evaluates their effectiveness in real-world applications, and discusses the
potential for their implementation in various embedded systems, emphasizing
security and user convenience. The findings underscore the feasibility and
advantages of employing these AI techniques to create secure, user-friendly
voice-activated systems.

###### Index Terms:

Audio Processing, Wake Words, Speaker Recognition, Embedded Systems, Synthetic
Data

##  I Introduction

In recent years, the popularization of voice-activated devices has transformed
the way users interact with technology. Devices like Amazon Alexa, Google
Home, and Apple Siri have popularized voice as a convenient interface for
accessing information, controlling smart home devices, and performing various
tasks. However, as the adoption of these devices grows, the concern for
security and privacy becomes an important discussed topic, since the
unauthorized access to these systems can lead to breaches of personal
information and unauthorized control of connected devices [3].

To address these concerns, the integration of wake words and speaker
recognition in voice-activated systems has emerged as a promising solution.
Wake words, such as ”Alexa” or ”Hey Siri” serve as verbal triggers that
activate the device and prepare it to receive commands. Speaker recognition,
on the other hand, involves identifying and verifying the identity of the
speaker based on their unique vocal characteristics. Together, these
technologies can enhance the security framework of voice-activated systems by
ensuring that only authorized users can interact with the device.

To develop the wake word detection model, synthetic data was employed to
augment the training dataset. The use of synthetic data is a crucial aspect of
our approach, as it allows for the creation of diverse and extensive training
examples, which are essential for building robust and accurate models. This
technique is particularly valuable when dealing with the scarcity of real-
world data, especially for specific wake words or in scenarios where data
privacy concerns limit the availability of authentic recordings. The synthetic
data generation process involved creating varied and realistic audio samples
that mimic the characteristics of natural speech, thus providing the model
with a broad range of vocal patterns and environmental conditions to learn
from.

In the following sections, we provide an overview of the current state of wake
word detection and speaker recognition technologies, describe the methodology
and experimental setup used in our research, present the results of our
experiments, and discuss the implications of our findings. Finally, we
conclude with a discussion of future research directions and the potential for
further advancements in this field.

##  II Background

###  II-A Voice-Activated Systems and Security Concerns

Voice-activated systems have become increasingly prevalent in everyday life,
offering hands-free convenience and intuitive interfaces for interacting with
technology [5]. These systems rely on Natural Language Processing (NLP) and
machine learning (ML) to understand and respond to user commands. However, the
widespread use of voice commands also introduces significant security and
privacy challenges. Unauthorized users can potentially access sensitive
information or control connected devices, leading to security breaches.

###  II-B Wake Words

Wake words are predefined phrases used to activate voice-activated systems,
signaling that the device is ready to receive commands [6]. This mechanism
serves as an initial security layer by requiring a specific trigger before any
interaction can occur. The detection of wake words must be both precise and
efficient, ensuring that the system responds only to intentional activations
while minimizing false positives and negatives.

###  II-C Speaker Recognition

Speaker recognition technology identifies and verifies individuals based on
their unique vocal characteristics. This process involves two main stages:
speaker identification and speaker verification. Speaker identification
determines the identity of the speaker from a group of known individuals,
while speaker verification confirms whether the speaker matches a claimed
identity [4].

Speaker recognition enhances security in voice-activated systems by
restricting access to authorized users. This capability is critical for
applications involving sensitive information or critical functions, where it
is essential to ensure that only specific individuals can issue commands.

##  III Methodology

###  III-A Data Collection and Synthetic Data Generation

In developing robust wake word detection and speaker recognition systems, data
quality and diversity are crucial. For wake word detection, the training
dataset included both real and synthetic audio samples. The real data
comprised a small variety of wake word samples from 3 different speakers.
However, synthetic data was generated to supplement the real dataset,
representing the majority part of the dataset. This synthetic data was created
using text-to-speech (TTS) systems and audio augmentation techniques, such as
described in [1], producing a wide range of samples that mimic natural
variations in pronunciation, background noise, and recording conditions. This
approach is particularly useful in our scenario where authentic data is
limited. The inclusion of synthetic data allows for more comprehensive
training, improving the model’s ability to generalize across different
speakers and environmental conditions.

###  III-B Wake Word Detection Model

The wake word detection system was built using a deep learning architecture
designed to handle the variability and complexity of speech data. The model
architecture incorporated a Convolutional Neural Network (CNN) for feature
extraction. The use of CNN layers enabled the model to learn hierarchical
representations of the audio data, which is crucial for accurately detecting
the presence of wake words, where, in this case, the wake word ”Hey, Gris!”
was chosen.

To enhance the performance of wake word models, we investigated and found that
including the use of synthetic data is beneficial to model performance when
using the Google proposed model c.

###  III-C Speaker Recognition System

The speaker recognition component aimed to authenticate users by verifying
their identity based on voice characteristics. For this purpose, we employed
the Titanet architecture [2], a state-of-the-art model designed for robust
speaker recognition. Titanet, known for its efficiency and accuracy, utilizes
an end-to-end deep learning approach that integrates feature extraction and
classification within a unified framework. This architecture, represented in
figure 1, is particularly well-suited for processing variable-length audio
segments and capturing the nuanced vocal characteristics that distinguish
different speakers.

Titanet consists of multiple layers, including residual blocks and attention
mechanisms, which enhance the model’s ability to focus on relevant features in
the audio data. The training process for Titanet involved a large dataset of
labeled speaker data, ensuring that the model could learn to accurately
differentiate between speakers. The use of attention mechanisms allowed
Titanet to dynamically weigh different parts of the input audio, improving its
capacity to handle variations in speaking style and environment.

Figure 1: NVIDIA TitaNet model architecture.

To enhance security and reduce the likelihood of spoofing or impersonation,
the system incorporated anti-spoofing measures, including the detection of
synthetic voices and other anomalies that could indicate unauthorized access
attempts.

##  IV Results

###  IV-A Empirical Evaluation

The effectiveness of our systems was assessed through an empirical evaluation
conducted in real-world environments. The primary focus was on evaluating the
response time and reliability of both the wake word detection and speaker
recognition models under various conditions, including different noise levels
and speaker variations. This practical testing aimed to simulate real-world
scenarios where these systems would typically be deployed, such as in homes or
offices.

Preliminary results indicate that the system effectively recognizes wake words
and accurately identifies speakers, even in challenging acoustic environments.
The use of synthetic data during training has significantly enhanced the
model’s robustness, allowing it to generalize well across different voices and
background noises. However, while these results are promising, they were
primarily based on qualitative observations. Future evaluations would benefit
from a more detailed quantitative analysis, which could offer a clearer
understanding of the system’s performance and highlight specific areas for
improvement. This approach would provide a more comprehensive assessment of
the system’s capabilities and help guide further refinement and optimization
efforts.

##  V Conclusion

This study has demonstrated the potential of integrating artificial
intelligence techniques, such as wake word detection and speaker recognition,
to enhance the security of voice-activated systems in embedded environments.
By leveraging wake words for system activation and speaker recognition for
user verification, the approach restricts system usage to authorized
individuals, thereby mitigating the risks of unauthorized access. The use of
synthetic data in training the wake word detection model was crucial, enabling
the system to generalize across diverse speakers and environmental conditions,
while the Titanet architecture provided robust speaker verification.

The empirical evaluation confirmed the effectiveness of these AI-driven
methodologies in real-world settings, showcasing their ability to create
secure and user-friendly voice-activated systems. The integration with ROS and
Docker streamlined the development and deployment processes, ensuring
adaptability across various hardware platforms. Future research could focus on
optimizing models for low-resource environments, enhancing anti-spoofing
measures, and expanding the use of synthetic data to further secure and refine
voice-activated technologies.

## References

  * [1] T. Ko, V. Peddinti, D. Povey, and S. Khudanpur.  Data augmentation for deep neural network acoustic modeling.  In Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), pages 3586–3589, 2015.
  * [2] Nithin Rao Koluguri, Taejin Park, and Boris Ginsburg.  Titanet: Neural model for speaker representation with 1d depth-wise separable convolutions and global context.  In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8102–8106. IEEE, 2022.
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