Face identification is a one-to-n matching problem where a captured face is compared to n samples in a database . In this work we propose two new methods for face identification . The first one combines entropy-like weighted Gabor features with the local normalization of Gabor features . These rejection classifiers are able to share discriminants in feature space and at the same time have high confidence in the rejection decision . In the face identification problem , it is possible that a pair of known individual faces are very dissimilar . It is very unlikely that both of them are close to an unknown face in the feature space . In this paper we investigate the gender classification problem from a non-conventional perspective . In particular , the analysis will aim to determine the factors critically affecting the accuracy of available technologies , better explaining differences between face-based identification and gender categorization . A novel challenging protocol is proposed , exploiting the dimensions of the Face Recognition Grand Challenge version 2.0 database ( FRGC2.0 ) . : Is Centroid Method a suitable approach ? . The paper presents a research and development of a face template creation sub-system , which serves a part of a face-based identification system . This system is intended to be used in surveillance camera systems . The main purpose of this research is to develop a complete system of face recognition . A Graphical User Interface ( GUI ) in MATLAB links all the steps starting from image preprocessing to face identification process . Evaluation was carried out using the images of 40 subjects from AT & T database and 10 subjects from JAFFE database producing 100 % accuracy with less than 1 minute average training time when inserting 1 to 10 new subjects into the system . Reconstructing the challenging human face identification process as a stability problem , we show that Electoral College can be used as a framework that provides a significantly enhanced face identification process by improving the accuracy of all holistic algorithms . The results are demonstrated by extensive experiments on benchmark face databases applying the Electoral College framework embedded with standard baseline and newly developed face identification algorithms . The method comprises the following steps : according to a first preset number of local binary pattern ( LBP ) features screened out by a face training sample , calculating LBP feature vectors corresponding to two to-be-identified face images same in size respectively , and obtaining a first preset number of first LBP feature vectors and second LBP feature vectors ; performing dimensionality reduction calculation on the first preset number of first LBP feature vectors and second LBP feature vectors , and obtaining a second preset number of first vectors and second vectors ; calculating distance measure between the first vectors and the second vectors ; if the distance measure is not greater than a preset threshold value , determining that the face images same in size are face images corresponding to the same person . As the face images can be identified through the multi-dimensional feature vectors , more reference data sources , rather than a single LBP feature value , are used during face image identification . Therefore , the face image identification accuracy is relatively high . To Know the Face Recognition Accuracy " The results generated illumination normalization retinex will be input to the image-based facial recognition system Eigenface PCA . Normalization illumination very important role in determining the success rate of the face image identification . By using the 5 faces of the sample data that each sample has 10 different facial conditions . Error correcting graph matching algorithm for face name identification used to match face graph from video and name graph from script of the video , Sensitivity analyses for co-occurrence relationship for face and name performed by coverage noise and intensity noise . As an application this proposed workis able to create a new experience on face name identification in videos . Face segmentation is an important processing step in a typically face-based person identification system . This paper presents a modified K-means clustering and fusion approach for face region extraction from colorful images of video sequences . After face detection , face is cropped from the actual image to remove the background and the resolution is set as 150 × 150 pixels . Eigenfaces and fisherfaces methods have been used for face identification part . Average images of subjects have been used as training set to improve the accuracy of identification . Points obtained form i.e. ASM may be used as an input to create the feature vectors of the face image . The proposed way of the face description was successfully used in written text recognition but it was never shown as a fully successful descriptor in the face identification purposes . The invention provides a face identifying system which comprises a monitoring system , wherein the monitoring system comprises a display machine , a controller , a first storage and a second storage , the monitoring system is connected with a human body induction device , a camera device , an image comparing device , an alarming device , a message sending device and a voice device , and the controller is composed of a procedure counter , an order register , an order decoder , a time sequence generator and an operating controller . The alarming device is a loud speaker alarming device , and the human body induction device is a thermal infrared human body inductor . Face alignment refers to detecting facial landmarks such as eye centers , nose tip , and mouth corners . 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 . The key challenge of face recognition is to develop effective feature representations for reducing intra - personal variations while enlarging inter - personal differences . In this paper , we show that it can be well solved with deep learning and using both face identification and verification signals as supervision . The Deep IDentification - verification features ( DeepID2 ) are learned with carefully designed deep convolutional networks . Learning identity - related component . When performing face verification or identification , is the only part which participates in the final similarity measure . Thus , the identity - related component should be as discriminative as possible . The accuracy of the facial feature location and extraction has largely influenced the results of final recognition . The paper has cleared the importance of facial feature extraction and recognition , then has applied the curvelet transform to the process of facial feature extraction and recognition . The paper has studied the implementation pre-process of facial feature extraction , and further analyzed the interception processing , processing methods and slicing based on the curvelet transform . Recent advancements in the area of facial recognition and verification introduced thepossibility of facial attendance for various use cases . In this paper we present a system namedas AttendXNet . The rapid increase in the availability of accurate 3D scanning devices has moved facial recognition and analysis into the 3D domain . 3D facial landmarks are often used as a simple measure of anatomy and it is crucial to have accurate algorithms for automatic landmark placement . Knowledge Representation Using High-Level Non-monotonic Reasoning.- Experiments on Information Retrieval Using Case-Based Reasoning.- A Probabilistic Exemplar-Based Model for Case-Based Reasoning.- Intensification and Diversification Strategies with Tabu Search : One-Machine Problem with Weighted Tardiness Objective.- A Methodology to Parallel the Temperature Cycle in Simulated Annealing.- Vertical Fragmentation and Allocation in Distributed Databases with Site Capacity Restrictions Using the Threshold Accepting Algorithm.- A Practical Approach for Logic Program Analysis and Transformation.- Experiments in Answer Sets Planning.- Robotics and Computer Vision.- Competitive Learning Methods for Efficient Vector Quantizations in a Speech Recognition Environment.- Facial Expression Recognition and Modeling for Virtual Intelligent Tutoring Systems.- Detection and Tracking of Facial Features in Video Sequences.- An Application of Behavior-Based Architecture for Mobile Robots Design.- Cooperative Simulated Annealing for Path Planning in Multi-robot Systems.- Learning Probabilistic Grid-Based Maps for Indoor Mobile Robots Using Ultrasonic and Laser Range Sensors.- Evolving Insect Locomotion Using Cooperative Genetic Programming.- On the Minimal Cooperative Attitude in Multi-robotics Systems.- Eigenfaces Versus Eigeneyes : document : Covariance Pooling for Facial Expression Recognition Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks . As the technology advances , vision systems will be able to sense subtle emotions and sentiments that humans can not . Geometric and appearance - based features are commonly used in facial expression recognition . In this study , we focus on spatial features , which are a type of geometric feature . It does this by using an R - CNN based approach and a modified AlexNet architecture which fuses intermediate convolutional layer outputs and adds separate fully - connected networks to predict each subtask . All - In - One Convolutional Neural Network for Face Analysis adds smile , age estimation and facial recognition to the former prediction tasks . We compare our results to all of these works . We also propose Interpolated Embeddings and Priority Class Sampling as complementary methods to improve the training of face representations . Our experiments on a challenging task of disguised face recognition confirm the advantages of the proposed methods . Disguised face recognition(FR ) is considered as one of the difficult and important problems in FR field . Rather than disguised modeling , a disguised face recognition algorithm based on local phase quantization(LPQ ) feature and biomimetic pattern recognition(BPR ) theory is presented in this paper . Disguised face recognition ( FR ) is considered as one of the difficult and important problems in FR field . Rather than disguised modeling , a disguised face recognition algorithm based on local feature fusion and geometry coverage is presented in this paper . Disguised face identification ( DFI ) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises . This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification . Since the training of deep learning architectures relies on large annotated datasets , two annotated facial key-points datasets are introduced . Considering the fact that most of the FRS operates in visible spectrum which is not optimal solution for disguise face recognition , we explore spectral imaging and thereby the intrinsic features of spectral imaging for disguise face recognition . The major contribution of this work lies in presenting new approach that extract the histogram features independently for the spectral band images using Histogram Oriented Gradient ( HOG ) method and learning the unique discriminative features in affine feature space for disguise face recognition problem in robust manner . We present an extensive experimental validation results in terms of verification rate to present the significance of our proposed method . In this paper , we propose an effective method for disguised face detection and recognition under the complex background . This method consists of two stages . This paper presents adaboost based disguised face discrimination method on embedded devices . The introduced method is simple and fast method for face detection and disguised face discrimination for security purpose . In this paper , we present a real-time deep neural network architecture ( called DiFRuNNT ) for disguised face verification . The proposed model consists of two neural networks , first one being a convolutional neural network ( CNN ) that predicts 20 facial key-points in the image and the second neural network classifies the subject based on the angles and ratios calculated from the predicted points . While self-supervision has usually been considered for tasks such as image classification , in this paper we aim at extending it to geometry-oriented tasks such as semantic matching and part detection . We do so by building on several recent ideas in unsupervised landmark detection . Our approach learns dense distinctive visual descriptors from an unlabeled dataset of images using synthetic image transformations . Furthermore , while CycleGAN uses two cycle consistency constraints , we show that the second one is detrimental in this application and we discard it , significantly simplifying the model . We show that these modifications improve the quality of the learned detector leading to state-of-the-art unsupervised landmark detection performance in a number of challenging human pose and facial landmark detection benchmarks . Our method exhaustively searches for the best and worst feature window position from a set of face images among all possible combinations using MLP . Then , it creates a non-rectangular emotion mask for feature selection in supervised facial expression recognition problem . It eliminates irrelevant data and improves the classification performance using backward feature elimination . In this paper , a deep-learning based architecture is proposed to estimate gender which includes both supervised and unsupervised facial feature extraction techniques and a deep network to fuse features and to classify them . As unsupervised techniques we have benefited deep-learning feature extraction method called convolutional neural network , and for supervised facial feature extraction we have used facial Action Units and Histogram of Oriented Gradients . Our proposed architecture has been tested on multiple datasets and for most of them the accuracy is considerably high . We propose a principled approach to supervised learning of facial landmarks detector based on the Deformable Part Models ( DPM ) . We treat the task of landmarks detection as an instance of the structured output classification . Automatic perception of facial expressions with scaling differences , pose variations and occlusions would greatly enhance natural human robot interaction . This research proposes unsupervised automatic facial point detection integrated with regression-based intensity estimation for facial action units ( AUs ) and emotion clustering to deal with such challenges . The proposed facial point detector is able to detect 54 facial points in images of faces with occlusions , pose variations and scaling differences using Gabor filtering , BRISK ( Binary Robust Invariant Scalable Keypoints ) , an Iterative Closest Point ( ICP ) algorithm and fuzzy c-means ( FCM ) clustering . BU-4DFE and BP4D-Spontaneous data sets were used for experiments . Retrieval results are used to achieve unsupervised facial expression recognition . The presented results outperform state-of-the-art retrieval schemes . We propose a novel approach to unsupervised facial image alignment . Differently from previous approaches , that are confined to affine transformations on either the entire face or separate patches , we extract a nonrigid mapping between facial images . We also demonstrate that the segments derived from the proposed unsupervised and phoneme segmentation strategies are similar to each other . This paper provides new insight into unsupervised facial motion segmentation and the impact of speech variability on emotion classification . In addition , it is also difficult to identify significant discriminating facial features that could represent the characteristic of each expression because of the subtlety and variability of facial expressions . In order to deal with the above challenges , this research proposes two novel approaches : unsupervised facial point detection and texture-based facial expression recognition with feature optimisation . First of all , unsupervised automatic facial point detection integrated with regression-based intensity estimation for facial Action Units ( AUs ) and emotion clustering is proposed to deal with challenges such as scaling differences , pose variations , and occlusions . This paper presents a dedicated approach to detect loop closures using visually salient patches . We introduce a novel , energy maximization based saliency detection technique which has been used for unsupervised landmark extraction . We explain how to learn the extracted landmarks on-the-fly and re-identify them . Along this track , most previous methods employ traditional machine learning techniques and construct classifiers based on geometric features . In the domain of unsupervised shape segmentation , there is one family of methods emphasizes the effectiveness of spectral analysis for 3D shape segmentation . Inspired by this , our framework aims to marry the powerfulness of deep neural network and spectral analysis for 3D shapes segmentation .
Face identification is a one-to-n matching problem where a captured face is compared to n samples in a database . In this work we propose two new methods for face identification . The first one combines entropy-like weighted Gabor features with the local normalization of Gabor features .
These rejection classifiers are able to share discriminants in feature space and at the same time have high confidence in the rejection decision . In the face identification problem , it is possible that a pair of known individual faces are very dissimilar . It is very unlikely that both of them are close to an unknown face in the feature space .
In this paper we investigate the gender classification problem from a non-conventional perspective . In particular , the analysis will aim to determine the factors critically affecting the accuracy of available technologies , better explaining differences between face-based identification and gender categorization . A novel challenging protocol is proposed , exploiting the dimensions of the Face Recognition Grand Challenge version 2.0 database ( FRGC2.0 ) .
: Is Centroid Method a suitable approach ? . The paper presents a research and development of a face template creation sub-system , which serves a part of a face-based identification system . This system is intended to be used in surveillance camera systems .
The main purpose of this research is to develop a complete system of face recognition . A Graphical User Interface ( GUI ) in MATLAB links all the steps starting from image preprocessing to face identification process . Evaluation was carried out using the images of 40 subjects from AT & T database and 10 subjects from JAFFE database producing 100 % accuracy with less than 1 minute average training time when inserting 1 to 10 new subjects into the system .
Reconstructing the challenging human face identification process as a stability problem , we show that Electoral College can be used as a framework that provides a significantly enhanced face identification process by improving the accuracy of all holistic algorithms . The results are demonstrated by extensive experiments on benchmark face databases applying the Electoral College framework embedded with standard baseline and newly developed face identification algorithms .
The method comprises the following steps : according to a first preset number of local binary pattern ( LBP ) features screened out by a face training sample , calculating LBP feature vectors corresponding to two to-be-identified face images same in size respectively , and obtaining a first preset number of first LBP feature vectors and second LBP feature vectors ; performing dimensionality reduction calculation on the first preset number of first LBP feature vectors and second LBP feature vectors , and obtaining a second preset number of first vectors and second vectors ; calculating distance measure between the first vectors and the second vectors ; if the distance measure is not greater than a preset threshold value , determining that the face images same in size are face images corresponding to the same person . As the face images can be identified through the multi-dimensional feature vectors , more reference data sources , rather than a single LBP feature value , are used during face image identification . Therefore , the face image identification accuracy is relatively high .
To Know the Face Recognition Accuracy " The results generated illumination normalization retinex will be input to the image-based facial recognition system Eigenface PCA . Normalization illumination very important role in determining the success rate of the face image identification . By using the 5 faces of the sample data that each sample has 10 different facial conditions .
Error correcting graph matching algorithm for face name identification used to match face graph from video and name graph from script of the video , Sensitivity analyses for co-occurrence relationship for face and name performed by coverage noise and intensity noise . As an application this proposed workis able to create a new experience on face name identification in videos .
Face segmentation is an important processing step in a typically face-based person identification system . This paper presents a modified K-means clustering and fusion approach for face region extraction from colorful images of video sequences .
After face detection , face is cropped from the actual image to remove the background and the resolution is set as 150 × 150 pixels . Eigenfaces and fisherfaces methods have been used for face identification part . Average images of subjects have been used as training set to improve the accuracy of identification .
Points obtained form i.e. ASM may be used as an input to create the feature vectors of the face image . The proposed way of the face description was successfully used in written text recognition but it was never shown as a fully successful descriptor in the face identification purposes .
The invention provides a face identifying system which comprises a monitoring system , wherein the monitoring system comprises a display machine , a controller , a first storage and a second storage , the monitoring system is connected with a human body induction device , a camera device , an image comparing device , an alarming device , a message sending device and a voice device , and the controller is composed of a procedure counter , an order register , an order decoder , a time sequence generator and an operating controller . The alarming device is a loud speaker alarming device , and the human body induction device is a thermal infrared human body inductor .
Face alignment refers to detecting facial landmarks such as eye centers , nose tip , and mouth corners . 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 .
The key challenge of face recognition is to develop effective feature representations for reducing intra - personal variations while enlarging inter - personal differences . In this paper , we show that it can be well solved with deep learning and using both face identification and verification signals as supervision . The Deep IDentification - verification features ( DeepID2 ) are learned with carefully designed deep convolutional networks .
Learning identity - related component . When performing face verification or identification , is the only part which participates in the final similarity measure . Thus , the identity - related component should be as discriminative as possible .
The accuracy of the facial feature location and extraction has largely influenced the results of final recognition . The paper has cleared the importance of facial feature extraction and recognition , then has applied the curvelet transform to the process of facial feature extraction and recognition . The paper has studied the implementation pre-process of facial feature extraction , and further analyzed the interception processing , processing methods and slicing based on the curvelet transform .
Recent advancements in the area of facial recognition and verification introduced thepossibility of facial attendance for various use cases . In this paper we present a system namedas AttendXNet .
The rapid increase in the availability of accurate 3D scanning devices has moved facial recognition and analysis into the 3D domain . 3D facial landmarks are often used as a simple measure of anatomy and it is crucial to have accurate algorithms for automatic landmark placement .
Knowledge Representation Using High-Level Non-monotonic Reasoning.- Experiments on Information Retrieval Using Case-Based Reasoning.- A Probabilistic Exemplar-Based Model for Case-Based Reasoning.- Intensification and Diversification Strategies with Tabu Search : One-Machine Problem with Weighted Tardiness Objective.- A Methodology to Parallel the Temperature Cycle in Simulated Annealing.- Vertical Fragmentation and Allocation in Distributed Databases with Site Capacity Restrictions Using the Threshold Accepting Algorithm.- A Practical Approach for Logic Program Analysis and Transformation.- Experiments in Answer Sets Planning.- Robotics and Computer Vision.- Competitive Learning Methods for Efficient Vector Quantizations in a Speech Recognition Environment.- Facial Expression Recognition and Modeling for Virtual Intelligent Tutoring Systems.- Detection and Tracking of Facial Features in Video Sequences.- An Application of Behavior-Based Architecture for Mobile Robots Design.- Cooperative Simulated Annealing for Path Planning in Multi-robot Systems.- Learning Probabilistic Grid-Based Maps for Indoor Mobile Robots Using Ultrasonic and Laser Range Sensors.- Evolving Insect Locomotion Using Cooperative Genetic Programming.- On the Minimal Cooperative Attitude in Multi-robotics Systems.- Eigenfaces Versus Eigeneyes :
document : Covariance Pooling for Facial Expression Recognition Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks .
As the technology advances , vision systems will be able to sense subtle emotions and sentiments that humans can not . Geometric and appearance - based features are commonly used in facial expression recognition . In this study , we focus on spatial features , which are a type of geometric feature .
It does this by using an R - CNN based approach and a modified AlexNet architecture which fuses intermediate convolutional layer outputs and adds separate fully - connected networks to predict each subtask . All - In - One Convolutional Neural Network for Face Analysis adds smile , age estimation and facial recognition to the former prediction tasks . We compare our results to all of these works .
We also propose Interpolated Embeddings and Priority Class Sampling as complementary methods to improve the training of face representations . Our experiments on a challenging task of disguised face recognition confirm the advantages of the proposed methods .
Disguised face recognition(FR ) is considered as one of the difficult and important problems in FR field . Rather than disguised modeling , a disguised face recognition algorithm based on local phase quantization(LPQ ) feature and biomimetic pattern recognition(BPR ) theory is presented in this paper .
Disguised face recognition ( FR ) is considered as one of the difficult and important problems in FR field . Rather than disguised modeling , a disguised face recognition algorithm based on local feature fusion and geometry coverage is presented in this paper .
Disguised face identification ( DFI ) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises . This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification . Since the training of deep learning architectures relies on large annotated datasets , two annotated facial key-points datasets are introduced .
Considering the fact that most of the FRS operates in visible spectrum which is not optimal solution for disguise face recognition , we explore spectral imaging and thereby the intrinsic features of spectral imaging for disguise face recognition . The major contribution of this work lies in presenting new approach that extract the histogram features independently for the spectral band images using Histogram Oriented Gradient ( HOG ) method and learning the unique discriminative features in affine feature space for disguise face recognition problem in robust manner . We present an extensive experimental validation results in terms of verification rate to present the significance of our proposed method .
In this paper , we propose an effective method for disguised face detection and recognition under the complex background . This method consists of two stages .
This paper presents adaboost based disguised face discrimination method on embedded devices . The introduced method is simple and fast method for face detection and disguised face discrimination for security purpose .
In this paper , we present a real-time deep neural network architecture ( called DiFRuNNT ) for disguised face verification . The proposed model consists of two neural networks , first one being a convolutional neural network ( CNN ) that predicts 20 facial key-points in the image and the second neural network classifies the subject based on the angles and ratios calculated from the predicted points .
While self-supervision has usually been considered for tasks such as image classification , in this paper we aim at extending it to geometry-oriented tasks such as semantic matching and part detection . We do so by building on several recent ideas in unsupervised landmark detection . Our approach learns dense distinctive visual descriptors from an unlabeled dataset of images using synthetic image transformations .
Furthermore , while CycleGAN uses two cycle consistency constraints , we show that the second one is detrimental in this application and we discard it , significantly simplifying the model . We show that these modifications improve the quality of the learned detector leading to state-of-the-art unsupervised landmark detection performance in a number of challenging human pose and facial landmark detection benchmarks .
Our method exhaustively searches for the best and worst feature window position from a set of face images among all possible combinations using MLP . Then , it creates a non-rectangular emotion mask for feature selection in supervised facial expression recognition problem . It eliminates irrelevant data and improves the classification performance using backward feature elimination .
In this paper , a deep-learning based architecture is proposed to estimate gender which includes both supervised and unsupervised facial feature extraction techniques and a deep network to fuse features and to classify them . As unsupervised techniques we have benefited deep-learning feature extraction method called convolutional neural network , and for supervised facial feature extraction we have used facial Action Units and Histogram of Oriented Gradients . Our proposed architecture has been tested on multiple datasets and for most of them the accuracy is considerably high .
We propose a principled approach to supervised learning of facial landmarks detector based on the Deformable Part Models ( DPM ) . We treat the task of landmarks detection as an instance of the structured output classification .
Automatic perception of facial expressions with scaling differences , pose variations and occlusions would greatly enhance natural human robot interaction . This research proposes unsupervised automatic facial point detection integrated with regression-based intensity estimation for facial action units ( AUs ) and emotion clustering to deal with such challenges . The proposed facial point detector is able to detect 54 facial points in images of faces with occlusions , pose variations and scaling differences using Gabor filtering , BRISK ( Binary Robust Invariant Scalable Keypoints ) , an Iterative Closest Point ( ICP ) algorithm and fuzzy c-means ( FCM ) clustering .
BU-4DFE and BP4D-Spontaneous data sets were used for experiments . Retrieval results are used to achieve unsupervised facial expression recognition . The presented results outperform state-of-the-art retrieval schemes .
We propose a novel approach to unsupervised facial image alignment . Differently from previous approaches , that are confined to affine transformations on either the entire face or separate patches , we extract a nonrigid mapping between facial images .
We also demonstrate that the segments derived from the proposed unsupervised and phoneme segmentation strategies are similar to each other . This paper provides new insight into unsupervised facial motion segmentation and the impact of speech variability on emotion classification .
In addition , it is also difficult to identify significant discriminating facial features that could represent the characteristic of each expression because of the subtlety and variability of facial expressions . In order to deal with the above challenges , this research proposes two novel approaches : unsupervised facial point detection and texture-based facial expression recognition with feature optimisation . First of all , unsupervised automatic facial point detection integrated with regression-based intensity estimation for facial Action Units ( AUs ) and emotion clustering is proposed to deal with challenges such as scaling differences , pose variations , and occlusions .
This paper presents a dedicated approach to detect loop closures using visually salient patches . We introduce a novel , energy maximization based saliency detection technique which has been used for unsupervised landmark extraction . We explain how to learn the extracted landmarks on-the-fly and re-identify them .
Along this track , most previous methods employ traditional machine learning techniques and construct classifiers based on geometric features . In the domain of unsupervised shape segmentation , there is one family of methods emphasizes the effectiveness of spectral analysis for 3D shape segmentation . Inspired by this , our framework aims to marry the powerfulness of deep neural network and spectral analysis for 3D shapes segmentation .
Fusion of local normalization and Gabor entropy weighted features for face identification Face Identification by a Cascade of Rejection Classifiers Understanding Critical Factors in Appearance-Based Gender Categorization Face template creation: Is Centroid Method a suitable approach? A MATLAB-based Convolutional Neural Network Approach for Face Recognition System A unified framework for improving the accuracy of all holistic face identification algorithms Face image identification method and device as well as server Perbaikan citra menggunakan metode retinex pada aplikasi monitoring ruangan berbasis webcam untuk mengetahui akurasi face recognition Automatic Face Identification OfcharacterinMovie by Ecgm Face extraction from video sequences by K-means clustering and fusion An improved face recognition method using Local Binary Pattern method A Feature Based Algorithm for Face Image Description Face identifying system Deep Multi-Center Learning for Face Alignment Deep Learning Face Representation by Joint Identification-Verification Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition The Research of Facial Feature Extraction and Recognition Based on Curvelet A Novel Approach for Facial Attendance:AttendXNet Multi-view Consensus CNN for 3D Facial Landmark Placement MICAI 2000 : advances in artificial intelligence : Mexican International Conference on Artificial Intelligence, Acapulco, Mexico, April 11-14, 2000 : proceedings Covariance Pooling for Facial Expression Recognition Greedy search for descriptive spatial face features Fine-Grained Head Pose Estimation Without Keypoints Face Representation Learning using Composite Mini-Batches Disguised Face Recognition Algorithm Based On Local Phase Quantization and Biomimetic Pattern Disguised Face Recognition Based on Local Feature Fusion and Biomimetic Pattern Recognition Disguised Face Identification (DFI) with Facial KeyPoints Using Spatial Fusion Convolutional Network Disguise Face Recognition Based On Spectral Imaging Disguised face detection and recognition under the complex background Adaboost based disguised face discrimination on embedded devices Disguised Facial Recognition Using Neural Networks Self-Supervised Learning of Geometrically Stable Features Through Probabilistic Introspection Learning Landmarks from Unaligned Data using Image Translation Intelligent pixels of interest selection with application to facial expression recognition using multilayer perceptron Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction Facial Landmarks Detector Learned by the Structured Output SVM Adaptive facial point detection and emotion recognition for a humanoid robot An effective methodology for dynamic 3D facial expression retrieval Unsupervised face alignment by robust nonrigid mapping Say Cheese vs. Smile: Reducing Speech-Related Variability for Facial Emotion Recognition Intelligent facial expression recognition with unsupervised facial point detection and evolutionary feature optimization An image-to-image loop-closure detection method based on unsupervised landmark extraction SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
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2014 2005 2012 2014 2016 2010 2015 2015 2014 2011 2017 2011 2014 2020 2014 2018 2010 2020 2018 2000 2018 2017 2018 2019 2012 2014 2017 2018 2014 2011 2018 2018 2019 2013 2017 2013 2015 2016 2009 2014 2016 2012 2017
CVPR Workshops ECCV Workshops (2) Neurocomputing NIPS ECCV ACCV (1) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ICCV Workshops CCBR ICVGIP CIBIM VISIGRAPP (Selected Papers) ICCV ACM Multimedia Intelligent Vehicles Symposium 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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