Fine mapping then successfully applies a weighted sum of errors on both surface data and the smoothness of deformation . Compared with previous methods , our approach makes robust alignment in a higher efficiency . This consistent parameterization also makes it possible for Principal Components Analysis ( PCA ) on the whole body as well as human body segments . Heterogeneous features are shown to be important . Line and point features provide accurate localization and are employed for robust alignment across disparate views . The challenges of change in pose , aspect , and appearances across two disparate observations are handled by combining a novel feature-based quasi-rigid alignment with flexible matching between two or more sequences . Computer Vision in Human-Computer Interaction.- Robust Face Alignment Based on Hierarchical Classifier Network.- EigenExpress Approach in Recognition of Facial Expression Using GPU.- Face Representation Method Using Pixel-to-Vertex Map ( PVM ) for 3D Model Based Face Recognition.- Robust Head Tracking with Particles Based on Multiple Cues Fusion.- Vision-Based Interpretation of Hand Gestures for Remote Control of a Computer Mouse.- Computing Emotion Awareness Through Facial Electromyography.- Silhouette-Based Method for Object Classification and Human Action Recognition in Video.- Voice Activity Detection Using Wavelet-Based Multiresolution Spectrum and Support Vector Machines and Audio Mixing Algorithm.- Action Recognition in Broadcast Tennis Video Using Optical Flow and Support Vector Machine.- FaceMouse : A Human-Computer Interface for Tetraplegic People.- Object Retrieval by Query with Sensibility Based on the KANSEI-Vocabulary Scale . However , its performance is sensitive to initial parameter values . In this paper , we propose a two-stage AAM for robust face alignment , which first fits an inner face-AAM model to the inner facial feature points of the face and then localizes the whole face and facial features by optimizing the whole face-AAM model parameters . Experiments show that the proposed face alignment method using two-stage AAM is more reliable to the background and the head pose than the standard AAM-based face alignment method . We propose the Edge-Embedded Multi-Dropout ( EEMD ) framework for real-time face alignment . The EEMD framework extracts facial edge features and explores multiple dropout architecture for locating facial landmarks . It also provides for easy extraction of moving objects in the scene . Our method incorporates locally adaptive warps which allows for robust image alignment even in the presence of parallax and without the aid of internal or external camera parameters . We accelerate the image registration process using commodity Graphics Processing Units ( GPUs ) to accomplish all of these tasks in near real-time with no external telemetry data . The discussed research topics on object recognition include image classification on ImageNet , face recognition , and video classification . The detection part covers general object detection on ImageNet , pedestrian detection , face landmark detection face alignment , and human landmark detection pose estimation . On the segmentation side , thearticle discusses the most recent progress on scene labeling , semantic segmentation , face parsing , human parsing and saliency detection . A novel face recognition system should be able to deal with various changes in face images , such as pose , illumination , and expression , among which pose variation is the most difficult one to deal with . Therefore , face registration ( alignment ) is the key of robust face recognition . If we can register face images into frontal views , the recognition task would be much easier . In this paper , we propose Occlusion Deep Alignment Network(ODAN ) , which is robust to occlusion . Occlusion detection and rough face alignment are employed simultaneously , where the occlusion detection of landmarks is treated as a regression problem . Based on occlusion information , localization offset scaling is employed to transform the difference between the groundtruth and the rough landmark estimation to another linear space to generate target positions for precise face alignment , reducing the difficulty of the regression task . Finally , the outputs of the proposed model are transformed back to Euclidean Space . Abstract . This work proposes a weighted joint sparse representation (WJSR)-based classification method for robust alignment-free face recognition , in which an image is represented by a set of scale-invariant feature transform descriptors . The proposed method considers the correlation and the reliability of the query descriptors . This paper proposes an oriented Hausdorff similarity ( OHS ) measure for robust object alignment . The OHS measure is introduced by replacing the distance concept of conventional Hausdoff distance ( HD ) algorithms by the similarity concept of the Hough transform ( HT ) . In response , some turned to larger 3D object data sets or using synthetically generated examples . We propose a far simpler alternative and show it to result in robust and accurate face alignment . section : A critique of facial landmark detection It is the preprocessor stage of many face analysis tasks like face animation , face beautification , and face recognition . A robust and accurate face alignment is still challenging in unconstrained scenarios , owing to severe occlusions and large appearance variations . Most conventional methods only use low - level handcrafted features and are not based on the prevailing deep neural networks , which limits their capacity to represent highly complex faces . Then , when considering a new target user , the classification model is computed by simply feeding the associated ( unlabeled ) sample distribution into the learned regression function . We evaluate the proposed approach in different applications : pain recognition and action unit detection using visual data and gestures classification using inertial measurements , demonstrating the generality of our method with respect to different input data types and basic classifiers . We also show the advantages of our approach in terms of accuracy and computational time both with respect to user-independent approaches and to previous personalization techniques . Action Unit ( AU ) detection from facial images is an important classification task in affective computing . However most existing approaches use carefully engineered feature extractors along with off-the-shelf classifiers . Facial action unit detection is an ongoing research area in image processing and computer vision . Different methods have been proposed in the recent years for accurate detection of action units in facial images for various views and lighting conditions . In this paper , we propose a new deep learning scheme that integrates convolutional neural networks with association rule mining . As part of a multimodal animated interface previously presented in [ 38 ] , in this paper we describe a method for dynamic recognition of displayed facial emotions on low resolution streaming images . First , we address the detection of Action Units of the Facial Action Coding System upon Active Shape Models and Gabor filters . Normalized outputs of the Action Unit recognition step are then used as inputs for a neural network which is based on real cognitive systems architecture , and consists on a habituation network plus a competitive network . To speed up expression evaluation process , pose estimation is carried out prior to feature extraction to select appropriate shape model . The major contribution of this paper is introducing a hybrid classification method which uses Ada-Boost for Action Unit classification and Temporal Rule-based classification for correcting Action Unit errors . The experimental results show that this hybrid classification method produces better performance than other classifier which ideally improves overall performance of the system . Recent perceptual judgment studies suggest that dynamics is critical to human perception of subtle changes in facial action . Our findings suggest that dynamics may be similarly critical to automatic action unit detection . By adding measures of facial dynamics to displacement , SVM classifiers achieved significant increases in accuracy for the action units investigated . Depending on context , these action units co-occur in specific patterns , and rarely in isolation . Yet , most existing methods for automatic action unit detection fail to exploit dependencies among them , and the corresponding facial features . To address this , we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection . In this paper , we present a common framework for realtime action unit detection and emotion recognition that we have developed for the emotion recognition and action unit detection sub-challenges of the FG 2011 Facial Expression Recognition and Analysis Challenge . For these tasks we employed a local appearance-based face representation approach using discrete cosine transform , which has been shown to be very effective and robust for face recognition . ( i ) LAUCN could transform the manual intervened factors ( i.e. gradient , pixels etc . ) , which identify the characteristics of two-dimensional shapes , to a hierarchical perceptron in multi-direction and multi-scale , while a small amount of samples are needed . ( ii ) LAUCN takes into account the core properties of facial AUs for detecting action unit more accurately , where the existence of strong co-occurrence structure indicates that the presence of one AU can be regarded as a prior for the presence of others so that the problem of subtle changes in appearance or geometry of the face will be well solved via exploiting this property . The experiments are conducted on the CK+ dataset , and the experimental results show that LAUCN can accomplish AU recognition satisfactorily and has achieved the expected results as well . In this paper , we propose a novel orthogonal complete discriminant locality preserving projections for facial feature extraction and recognition ( OCDLPP ) . All training samples are projected into the range of a so-called locality preserving total scatterto reduce dimensionality without loss of discriminative information . In this paper , a novel method for facial feature extraction and recognition using an optimized combination of Deformable Parts Model ( DPM ) and Dense Scale Invariant Feature Transform ( D-SIFT ) is proposed . Real time face recognition systems pose challenges such as the speed and responsiveness . We propose an approach to 3D facial landmark localization based on two-step keypoint detection . In this paper , the facial landmarking problem is devided into two subproblems that are solved independently , keypoint detection and labeling . Abstract 3D facial landmark detection is a crucial step for many computer vision applications , such as 3D facial expression analysis , 3D face recognition , and 3D cephalometry . Pose variations , expression changes and self-occlusion yet make 3D facial landmark detection a very challenging task . In this paper , we propose a novel 3D Facial Landmark Localization Network ( 3DLLN ) , which is robust to the above challenges . The current facial landmark methods in 3D involve amathematically complex and time-consumingworkflow involving semilandmark sliding tasks . This paper proposes a homologous multi-point warping for 3D facial landmarking , which is verified experimentally on each of the target objects in a given dataset using 500 landmarks ( 16 anatomical fixed points and 484 sliding semi-landmarks ) . This is achieved by building a template mesh as a reference object and applying this template to each of the targets in three datasets using an artificial deformation approach . To support the ability of our method to reliably reconstruct 3D shapes , we introduce a simple method for head pose estimation using a single image that reaches higher accuracy than the state of the art . Comparison of 3D face landmarks localization with the available state of the art further supports the feasibility of a single-step face shape estimation . The code , trained models and our 3D annotations will be made available to the research community . We present our graph matching approach for 3D facial feature localisation . The work here uses a basic graph model ( three vertices and three arcs ) to locate the inner eye corners and the nose tip simultaneously . There are many advanced methods in 2D solution to optimize the detection on the image plane . 3D solution employs a 3D dense face model for 3D facial landmarks estimation . Though the two solutions have made great progress , there are limitations hindering face alignment accuracy 's further improvement . We also propose a new Cross‐Dimension Annotations Conversion ( CDAC ) method to map facial landmarks from 2D to 3DA‐2D coordinates and vice versa . The experimental results showed that CDAC combined with MBCR outperforms the‐state‐of‐the‐art methods in 3DA‐2D facial landmark localization . Moreover , CDAC can run efficiently at up to 110 fps on a 3.4 GHz‐CPU workstation . In this paper , we present a novel approach to automatic 3D facial landmarking using 2D Gabor wavelets . Our algorithm considers the face to be a surface and uses map projections to derive 2D features from raw data . Our method uses only 2D images as input to the proposed CNN architecture . We show how the related task of 3D facial landmark localisation can be incorporated into the proposed framework and help improve reconstruction quality , especially for the cases of large poses and facial expressions . We report results for a large number of experiments on both controlled and completely unconstrained images from the web , illustrating that our method outperforms prior work on single image 3D face reconstruction by a large margin .
Fine mapping then successfully applies a weighted sum of errors on both surface data and the smoothness of deformation . Compared with previous methods , our approach makes robust alignment in a higher efficiency . This consistent parameterization also makes it possible for Principal Components Analysis ( PCA ) on the whole body as well as human body segments .
Heterogeneous features are shown to be important . Line and point features provide accurate localization and are employed for robust alignment across disparate views . The challenges of change in pose , aspect , and appearances across two disparate observations are handled by combining a novel feature-based quasi-rigid alignment with flexible matching between two or more sequences .
Computer Vision in Human-Computer Interaction.- Robust Face Alignment Based on Hierarchical Classifier Network.- EigenExpress Approach in Recognition of Facial Expression Using GPU.- Face Representation Method Using Pixel-to-Vertex Map ( PVM ) for 3D Model Based Face Recognition.- Robust Head Tracking with Particles Based on Multiple Cues Fusion.- Vision-Based Interpretation of Hand Gestures for Remote Control of a Computer Mouse.- Computing Emotion Awareness Through Facial Electromyography.- Silhouette-Based Method for Object Classification and Human Action Recognition in Video.- Voice Activity Detection Using Wavelet-Based Multiresolution Spectrum and Support Vector Machines and Audio Mixing Algorithm.- Action Recognition in Broadcast Tennis Video Using Optical Flow and Support Vector Machine.- FaceMouse : A Human-Computer Interface for Tetraplegic People.- Object Retrieval by Query with Sensibility Based on the KANSEI-Vocabulary Scale .
However , its performance is sensitive to initial parameter values . In this paper , we propose a two-stage AAM for robust face alignment , which first fits an inner face-AAM model to the inner facial feature points of the face and then localizes the whole face and facial features by optimizing the whole face-AAM model parameters . Experiments show that the proposed face alignment method using two-stage AAM is more reliable to the background and the head pose than the standard AAM-based face alignment method .
We propose the Edge-Embedded Multi-Dropout ( EEMD ) framework for real-time face alignment . The EEMD framework extracts facial edge features and explores multiple dropout architecture for locating facial landmarks .
It also provides for easy extraction of moving objects in the scene . Our method incorporates locally adaptive warps which allows for robust image alignment even in the presence of parallax and without the aid of internal or external camera parameters . We accelerate the image registration process using commodity Graphics Processing Units ( GPUs ) to accomplish all of these tasks in near real-time with no external telemetry data .
The discussed research topics on object recognition include image classification on ImageNet , face recognition , and video classification . The detection part covers general object detection on ImageNet , pedestrian detection , face landmark detection face alignment , and human landmark detection pose estimation . On the segmentation side , thearticle discusses the most recent progress on scene labeling , semantic segmentation , face parsing , human parsing and saliency detection .
A novel face recognition system should be able to deal with various changes in face images , such as pose , illumination , and expression , among which pose variation is the most difficult one to deal with . Therefore , face registration ( alignment ) is the key of robust face recognition . If we can register face images into frontal views , the recognition task would be much easier .
In this paper , we propose Occlusion Deep Alignment Network(ODAN ) , which is robust to occlusion . Occlusion detection and rough face alignment are employed simultaneously , where the occlusion detection of landmarks is treated as a regression problem . Based on occlusion information , localization offset scaling is employed to transform the difference between the groundtruth and the rough landmark estimation to another linear space to generate target positions for precise face alignment , reducing the difficulty of the regression task . Finally , the outputs of the proposed model are transformed back to Euclidean Space .
Abstract . This work proposes a weighted joint sparse representation (WJSR)-based classification method for robust alignment-free face recognition , in which an image is represented by a set of scale-invariant feature transform descriptors . The proposed method considers the correlation and the reliability of the query descriptors .
This paper proposes an oriented Hausdorff similarity ( OHS ) measure for robust object alignment . The OHS measure is introduced by replacing the distance concept of conventional Hausdoff distance ( HD ) algorithms by the similarity concept of the Hough transform ( HT ) .
In response , some turned to larger 3D object data sets or using synthetically generated examples . We propose a far simpler alternative and show it to result in robust and accurate face alignment . section : A critique of facial landmark detection
It is the preprocessor stage of many face analysis tasks like face animation , face beautification , and face recognition . A robust and accurate face alignment is still challenging in unconstrained scenarios , owing to severe occlusions and large appearance variations . Most conventional methods only use low - level handcrafted features and are not based on the prevailing deep neural networks , which limits their capacity to represent highly complex faces .
Then , when considering a new target user , the classification model is computed by simply feeding the associated ( unlabeled ) sample distribution into the learned regression function . We evaluate the proposed approach in different applications : pain recognition and action unit detection using visual data and gestures classification using inertial measurements , demonstrating the generality of our method with respect to different input data types and basic classifiers . We also show the advantages of our approach in terms of accuracy and computational time both with respect to user-independent approaches and to previous personalization techniques .
Action Unit ( AU ) detection from facial images is an important classification task in affective computing . However most existing approaches use carefully engineered feature extractors along with off-the-shelf classifiers .
Facial action unit detection is an ongoing research area in image processing and computer vision . Different methods have been proposed in the recent years for accurate detection of action units in facial images for various views and lighting conditions . In this paper , we propose a new deep learning scheme that integrates convolutional neural networks with association rule mining .
As part of a multimodal animated interface previously presented in [ 38 ] , in this paper we describe a method for dynamic recognition of displayed facial emotions on low resolution streaming images . First , we address the detection of Action Units of the Facial Action Coding System upon Active Shape Models and Gabor filters . Normalized outputs of the Action Unit recognition step are then used as inputs for a neural network which is based on real cognitive systems architecture , and consists on a habituation network plus a competitive network .
To speed up expression evaluation process , pose estimation is carried out prior to feature extraction to select appropriate shape model . The major contribution of this paper is introducing a hybrid classification method which uses Ada-Boost for Action Unit classification and Temporal Rule-based classification for correcting Action Unit errors . The experimental results show that this hybrid classification method produces better performance than other classifier which ideally improves overall performance of the system .
Recent perceptual judgment studies suggest that dynamics is critical to human perception of subtle changes in facial action . Our findings suggest that dynamics may be similarly critical to automatic action unit detection . By adding measures of facial dynamics to displacement , SVM classifiers achieved significant increases in accuracy for the action units investigated .
Depending on context , these action units co-occur in specific patterns , and rarely in isolation . Yet , most existing methods for automatic action unit detection fail to exploit dependencies among them , and the corresponding facial features . To address this , we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection .
In this paper , we present a common framework for realtime action unit detection and emotion recognition that we have developed for the emotion recognition and action unit detection sub-challenges of the FG 2011 Facial Expression Recognition and Analysis Challenge . For these tasks we employed a local appearance-based face representation approach using discrete cosine transform , which has been shown to be very effective and robust for face recognition .
( i ) LAUCN could transform the manual intervened factors ( i.e. gradient , pixels etc . ) , which identify the characteristics of two-dimensional shapes , to a hierarchical perceptron in multi-direction and multi-scale , while a small amount of samples are needed . ( ii ) LAUCN takes into account the core properties of facial AUs for detecting action unit more accurately , where the existence of strong co-occurrence structure indicates that the presence of one AU can be regarded as a prior for the presence of others so that the problem of subtle changes in appearance or geometry of the face will be well solved via exploiting this property . The experiments are conducted on the CK+ dataset , and the experimental results show that LAUCN can accomplish AU recognition satisfactorily and has achieved the expected results as well .
In this paper , we propose a novel orthogonal complete discriminant locality preserving projections for facial feature extraction and recognition ( OCDLPP ) . All training samples are projected into the range of a so-called locality preserving total scatterto reduce dimensionality without loss of discriminative information .
In this paper , a novel method for facial feature extraction and recognition using an optimized combination of Deformable Parts Model ( DPM ) and Dense Scale Invariant Feature Transform ( D-SIFT ) is proposed . Real time face recognition systems pose challenges such as the speed and responsiveness .
We propose an approach to 3D facial landmark localization based on two-step keypoint detection . In this paper , the facial landmarking problem is devided into two subproblems that are solved independently , keypoint detection and labeling .
Abstract 3D facial landmark detection is a crucial step for many computer vision applications , such as 3D facial expression analysis , 3D face recognition , and 3D cephalometry . Pose variations , expression changes and self-occlusion yet make 3D facial landmark detection a very challenging task . In this paper , we propose a novel 3D Facial Landmark Localization Network ( 3DLLN ) , which is robust to the above challenges .
The current facial landmark methods in 3D involve amathematically complex and time-consumingworkflow involving semilandmark sliding tasks . This paper proposes a homologous multi-point warping for 3D facial landmarking , which is verified experimentally on each of the target objects in a given dataset using 500 landmarks ( 16 anatomical fixed points and 484 sliding semi-landmarks ) . This is achieved by building a template mesh as a reference object and applying this template to each of the targets in three datasets using an artificial deformation approach .
To support the ability of our method to reliably reconstruct 3D shapes , we introduce a simple method for head pose estimation using a single image that reaches higher accuracy than the state of the art . Comparison of 3D face landmarks localization with the available state of the art further supports the feasibility of a single-step face shape estimation . The code , trained models and our 3D annotations will be made available to the research community .
We present our graph matching approach for 3D facial feature localisation . The work here uses a basic graph model ( three vertices and three arcs ) to locate the inner eye corners and the nose tip simultaneously .
There are many advanced methods in 2D solution to optimize the detection on the image plane . 3D solution employs a 3D dense face model for 3D facial landmarks estimation . Though the two solutions have made great progress , there are limitations hindering face alignment accuracy 's further improvement .
We also propose a new Cross‐Dimension Annotations Conversion ( CDAC ) method to map facial landmarks from 2D to 3DA‐2D coordinates and vice versa . The experimental results showed that CDAC combined with MBCR outperforms the‐state‐of‐the‐art methods in 3DA‐2D facial landmark localization . Moreover , CDAC can run efficiently at up to 110 fps on a 3.4 GHz‐CPU workstation .
In this paper , we present a novel approach to automatic 3D facial landmarking using 2D Gabor wavelets . Our algorithm considers the face to be a surface and uses map projections to derive 2D features from raw data .
Our method uses only 2D images as input to the proposed CNN architecture . We show how the related task of 3D facial landmark localisation can be incorporated into the proposed framework and help improve reconstruction quality , especially for the cases of large poses and facial expressions . We report results for a large number of experiments on both controlled and completely unconstrained images from the web , illustrating that our method outperforms prior work on single image 3D face reconstruction by a large margin .
Analysis of segmented human body scans Robust Object Matching for Persistent Tracking with Heterogeneous Features Computer Vision in Human-Computer Interaction, ECCV 2006 Workshop on HCI, Graz, Austria, May 13, 2006, Proceedings Reliable Face Alignment Using Two-Stage AAM Edge-Embedded Multi-Dropout Framework for Real-Time Face Alignment A Unified Approach to GPU-Accelerated Aerial Video Enhancement Techniques Deep Learning in Object Recognition, Detection, and Segmentation 3D head tracking and pose-robust 2D texture map-based face recognition using a simple ellipsoid model Occlusion Deep Alignment Network Weighted joint sparse representation-based classification method for robust alignment-free face recognition Two-dimensional object alignment based on the robust oriented Hausdorff similarity measure FacePoseNet: Making a Case for Landmark-Free Face Alignment Deep Multi-Center Learning for Face Alignment Learning Personalized Models for Facial Expression Analysis and Gesture Recognition A multi-label convolutional neural network approach to cross-domain action unit detection Facial Action Unit Recognition Using Data Mining Integrated Deep Learning Dynamic Facial Emotion Recognition Oriented to HCI Applications Multi-view Face Expression Recognition—A Hybrid Method Abstract: Towards Automatic Coding of Facial Expression Joint Facial Action Unit Detection and Feature Fusion: A Multi-Conditional Learning Approach A common framework for real-time emotion recognition and facial action unit detection Lite AU convolution network driven by a small amount of samples Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition Facial feature extraction and recognition using Deformable Parts Model and DSIFT 3D Facial Landmark Localization Based on Two-Step Keypoint Detection Deep 3D Facial Landmark Localization on position maps Landmark-based homologous multi-point warping approach to 3D facial recognition using multiple datasets Regressing a 3D Face Shape from a Single Image 3D Facial Landmark Localisation by Matching Simple Descriptors A Survey for 2D and 3D Face Alignment A Cross‐Dimension Annotations Method for 3D Structural Facial Landmark Extraction An Automatic 3D Facial Landmarking Algorithm Using 2D Gabor Wavelets Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
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