The further incorporation of entropy weights ensures that the facial features have a greater role in the soft decision-making as compared with the background features . The highlights of the authors ’ work are : ( i ) The development of a novel highly efficient fuzzy SIFT descriptor matching tool ; ( ii ) incorporation of feature entropy weights to highlight the contribution of facial features ; ( iii ) application to robust face recognition from uncropped images having diverse backgrounds with a single template for each subject . The authors thus allow for weak supervision of the face recognition experiment and obtain high accuracy for 20 subjects of the CALTECH-256 face database , 133 subjects of the labelled faces for the wild dataset and 994 subjects of the FERET database , with state-of-the-art comparisons indicating the supremacy of the authors ’ approach . This paper summarizes the various components of face recognition research conducted at the IRIS Lab . First , fusion of visual and thermal infrared ( IR ) images for robust face recognition is discussed . Two techniques are implemented : data fusion and decision fusion . Abstract In this paper , we consider the robust face recognition problem via matrix regression-based classification with block-norm(MRC-block ) . Specifically , we propose an inner-class sparse representation classification approach in which images are expressed as matrices instead of vectors and adopted to encode more discriminative information than other regression-based methods . Systems.- Toward a Unified Probabilistic Framework for Object Recognition and Segmentation.- Distributed Multi-camera Surveillance for Aircraft Servicing Operations.- Mining Paths of Complex Crowd Scenes.- Geometric and Photometric Analysis for Interactively Recognizing Multicolor or Partially Occluded Objects.- A Three-Level Graph Based Interactive Volume Segmentation System.- Self-organizing Deformable Model : A New Method for Fitting Mesh Model to Given Object Surface.- Image-Based Deformation of Objects in Real Scenes.- Comparing Sphere-Tree Generators and Hierarchy Updates for Deformable Objects Collision Detection.- Simulating Complex Organ Interactions : Evaluation of a Soft Tissue Discrete Model.- Face Verification in Polar Frequency Domain : A Biologically Motivated Approach.- Face Alignment and Adaptive Weight Assignment for Robust Face Recognition.- Face Detection in Low-Resolution Images.- Investigating the Impact of Face Categorization on Recognition Performance.- A Novel Approach on Silhouette Based Human Motion Analysis for Gait Recognition.- A Hybrid HMM/DPA Adaptive Gesture Recognition Method.- Hifocon : Object and Dimensional Coherence and Correlation in Multidimensional Visualization.- Efficient Compression of Visibility Sets.- Rendering Optimizations Guided by Head-Pose Estimates and Their Uncertainty.- Acceptance of Visual Search Interfaces for the Web - Design and Empirical Evaluation of a Book Search Interface.- Distributed and Collaborative Biomedical Data Exploration.- Image Database Navigation : A Globe-Al Approach.- Viewpoint Interpolation Using an Ellipsoid Head Model for Video Teleconferencing.- Real-Time Video Annotations for Augmented Reality.- A Tree-Structured Model of Visual Appearance Applied to Gaze Tracking.- Emotional Expression in Virtual Agents Through Body Language.- Visual Tracking for Seamless 3D Interactions in Augmented Reality.- ARISupport -s ETDRS.- Using Multimodal MR Data for Segmentation and Topology Recovery of the Cerebral Superficial Venous Tree.- Loop Removal from Colon Central Path Through Skeleton Scale-Space Tracking.- Multiscale Segmentation of HRCT Images Using Bipolar Incoherent Filtering.- Building Statistical Atlas of White Matter Fiber Tract Based on Vector/Tensor Field Reconstruction in Diffusion Tensor MRI.- Interactive 3D Heart Chamber Partitioning with a New Marker-Controlled Watershed Algorithm.- Inferring Cause/Effect Relationships in Multi-sensor Ambient Intelligence Systems.- Toward a Unified Probabilistic Framework for Object Recognition and Segmentation.- Distributed Multi-camera Surveillance for Aircraft Servicing Operations.- Mining Paths of Complex Crowd Scenes.- Geometric and Photometric Analysis for Interactively Recognizing Multicolor or Partially Occluded Objects.- A Three-Level Graph Based Interactive Volume Segmentation System.- Self-organizing Deformable Model : A New Method for Fitting Mesh Model to Given Object Surface.- Image-Based Deformation of Objects in Real Scenes.- Comparing Sphere-Tree Generators and Hierarchy Updates for Deformable Objects Collision Detection.- Simulating Complex Organ Interactions : Evaluation of a Soft Tissue Discrete Model.- Face Verification in Polar Frequency Domain : A Biologically Motivated Approach.- Face Alignment and Adaptive Weight Assignment for Robust Face Recognition.- Face Detection in Low-Resolution Images.- Investigating the Impact of Face Categorization on Recognition Performance.- A Novel Approach on Silhouette Based Human Motion Analysis for Gait Recognition.- A Hybrid HMM/DPA Adaptive Gesture Recognition Method.- Hifocon : Object and Dimensional Coherence and Correlation in Multidimensional Visualization.- Efficient Compression of Visibility Sets.- Rendering Optimizations Guided by Head-Pose Estimates and Their Uncertainty.- Acceptance of Visual Search Interfaces for the Web - Design and Empirical Evaluation of a Book Search Interface.- Distributed and Collaborative Biomedical Data Exploration.- Image Database Navigation : A Globe-Al Approach.- Viewpoint Interpolation Using an Ellipsoid Head Model for Video Teleconferencing.- Real-Time Video Annotations for Augmented Reality.- A Tree-Structured Model of Visual Appearance Applied to Gaze Tracking.- Emotional Expression in Virtual Agents Through Body Language.- Visual Tracking for Seamless 3D Interactions in Augmented Reality.- ARISupport - Interaction Support for Augmented Reality Systems.- Background Updating for Visual Surveillance.- Pattern Discovery for Video Surveillance.- Real-Time Crowd Density Estimation Using Images.- Automatic Robust Background Modeling Using Multivariate Non-parametric Kernel Density Estimation for Visual Surveillance.- Recognition of Complex Human Behaviors in Pool Environment Using Foreground Silhouette.- Adaptive Background Subtraction with Multiple Feedbacks for Video Surveillance.- A Vectorial Self-dual Morphological Filter Based on Total Variation Minimization.- However , portraits are subject to several complexities such as aesthetic sensibilities of the artist or social standing of the sitter . Thus , for robust automated face recognition , it becomes important to model the characteristics of the artist . In this paper , we focus on modeling the styles of artists by considering case studies involving Renaissance art-works . AbstractAutomatic human face recognition is already in research from some decades due to its application in different fields . But there is no unique technique that is very much worthwhile for robust automatic human face recognition , suitable for all possible situations . In this paper , a new technique is proposed , which is a holistic approach , and it is based on ‘ one to all ’ comparison method . A multiple observation-based scheme ( MObS ) is described for robust facial recognition , and a novel object recognition method called kernel mutual subspace method ( KMS ) is proposed . The mutual sub-space method ( MSM ) proposed by ( Maeda , et al. , 1999 ) is a powerful method for recognizing facial images . Robust human face recognition is one of the most important open tasks in computer vision . This study deals with a challenging subproblem of face recognition : the aim of the paper is to give a precise estimation for the 3D head pose . We describe a project to trial and develop enhanced surveillance technologies for public safety . A key technology is robust recognition of faces from low-resolution CCTV footage where there may be as few as 12 pixels between the eyes . Current commercial face recognition systems require 60 - 90 pixels between the eyes as well as tightly controlled image capture conditions . Robust vision-based face recognition is one of most challenging tasks for robots . Recently the sparse representation-based classification ( SRC ) has been proposed to solve the problem . These representations have shown strong performance for recognition of imagery and video in - the - wild in unconstrained datasets , with recent approaches demonstrating capabilities that exceed human performance on the well known Labeled Faces in the Wild dataset . The problem of face recognition may be described in terms of face verification and face identification . Face verification involves computing a one - to - one similarity between a probe image and a reference image , to determine if two image observations are of the same subject . Similarly , the YouTubeFaces dataset contains 3425 videos of 1595 subjects , and compares pairs of videos of subjects for verification . These datasets have set the established standard for face recognition research , with steadily increasing performance . Recently , protocols for face identification have been introduced for LFW to address the performance evaluation for identification on a common dataset . Figure 2 . Face images in the IJB - A dataset , sorted by their scores ( values of e in Eq . 2 ) from a single attention block trained in the face recognition task . The faces in the top , middle and bottom rows are sampled from the faces with scores in the highest 5 % , a 10 % window centered at the median , and the lowest 5 % , respectively . However , it can not guarantee the identity by itself . To further enhance the identity keeping property , we add constrains on the feature representation extracted for face recognition or verification . In face verification task , two faces are from the same person when the distance between two features are smaller than ceratain threshold . In cognitive science , there are a lot of studies on analyzing the mechanisms of face processing of neurons in visual cortex . Inspired by those works , we analyze the behaviours of neurons in artificial neural networks in a attempt to explain face recognition process in deep nets , what information is encoded in neurons , and how robust they are to corruptions . Our study is based on a high - performance deep convolutional neural network ( deep ConvNet ) , referred to as DeepID2 + , proposed in this paper . document : Robust Face Detection via Learning Small Faces on Hard Images Recent anchor - based deep face detectors have achieved promising performance , but they are still struggling to detect hard faces , such as small , blurred and partially occluded faces . This will calculate the Euclidean distance of the both the query and the dataset image . To investigate the effectiveness of different attributes and vital factors essential for face retrieval . By further enhancing , the accuracy of image retrieval can be improved . These techniques are implemented and their performances are investigated using f rontal facial images from the ORL database . The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image retrieval , it still encounters problems especially in implementation ; e.g. Floating point operation and decomposition speed . We use the advantages of lifting scheme , a spatial approach for constructing wavelet filters , which provides feasible alternative for problems facing its classical counterpart . Face image retrieval systems have attained much importance in recent times , due to many real time application demands it . Normally , image retrieval has become a challenging issue in the real world applications because the image collections are often high dimensional . This process of retrieving the images from the big images dataset is called as content based image retrieval ( CBIR ) . These CBIR techniques are adopted recently in many image based applications like fingerprint matching image retrieval , face based image retrieval , and attribute based image retrieval . The area of interest during this review paper is face based image retrieval . The system achieved good clustering accuracy when tested on different image sets and by different users . Moreover , features such as adding new images , face cluster navigation and face based image retrieval are added that greatly improve the usability of the system . It also facilitates efficient manual manipulations of clustering results . Mining web facial images on the Internet has emerged as a promising paradigm towards auto face annotation . Therefore , a search assistance tool has been proposed , which helps the face image search based on annotation more efficient . In a face image database which has several face images of many personalities and the labels are not placed properly , which means some images are without label and some are without proper label , in this situation annotation based search will fail . The similarities found from soft biometrics can also be useful as a source of evidence in courts of law because they are more descriptive than the numerical matching scores generated by a traditional face matcher . We propose to utilize demographic information ( e.g. , gender and ethnicity ) and facial marks ( e.g. , scars , moles , and freckles ) for improving face image matching and retrieval performance . An automatic facial mark detection method has been developed that uses ( 1 ) the active appearance model for locating primary facial features ( e.g. , eyes , nose , and mouth ) , ( 2 ) the Laplacian-of-Gaussian blob detection , and ( 3 ) morphological operators . Linear Discriminant Analysis ( LDA ) is a popular feature extraction technique for face image recognition and retrieval . However , It often suffers from the small sample size problem when dealing with the high dimensional face data . The feature vectors from the face image database are generated by using Gabor wavelet ( GW ) , wavelet Transformation ( WT ) , and Principal Component Analysis ( PCA ) . For the face image retrieval purpose , Artificial Neural Network ( ANN ) is adopted and its performance on the retrieval process is evaluated with PCA , WT , GW and their fusion as a feature vector . The query image is recognized from the faces returned by retrieval process by using Support Vector Machine ( SVM ) . The apparatus for registering face identification features can eliminate time and effort for manually retrieving and preparing face images by being provided with a face image retrieving portion 11 for retrieving a face image of a person via a network using the person 's name as a keyword , a face feature extracting portion 12 for extracting features , which greatly influence identification of a person , from the face images retrieved by the face image retrieving portion 11 , and a celebrity name , face image and feature database 13 for registering the face images retrieved by the face image retrieving portion 11 and the face features extracted by the face feature extracting portion 12 in a state where they are associated with the person 's names . Face image-video retrieval refers to retrieving videos of a specific person with image query or searching face images of one person by using a video clip query . It has attracted much attention for broad applications like suspect tracking and identifying . We present the adaptive manifold self-organising map ( AMSOM ) for a face retrieval system . Our experimental results show that it has an excellent potential for face retrieval applications . As compared to the more traditional sub-space self-organising map , the results in many cases are better . In this paper , we propose a novel bagging null space locality preserving discriminant analysis ( bagNLPDA ) method for facial feature extraction and recognition . The bagNLPDA method first projects all the training samples into the range space of a so-called locality preserving total scatter matrix without losing any discriminative information . For image classification tasks , deep convolutional neural networks have achieved state-of-the-art results , and many activation functions have been proposed to enhance the classification performance of these networks . We explore the performance of multiple maxout activation variants on image classification , facial recognition and verification tasks using convolutional neural networks . Our experiments compare rectified linear unit , leaky rectified linear unit , scaled exponential linear unit , and hyperbolic tangent to four maxout variants .
The further incorporation of entropy weights ensures that the facial features have a greater role in the soft decision-making as compared with the background features . The highlights of the authors ’ work are : ( i ) The development of a novel highly efficient fuzzy SIFT descriptor matching tool ; ( ii ) incorporation of feature entropy weights to highlight the contribution of facial features ; ( iii ) application to robust face recognition from uncropped images having diverse backgrounds with a single template for each subject . The authors thus allow for weak supervision of the face recognition experiment and obtain high accuracy for 20 subjects of the CALTECH-256 face database , 133 subjects of the labelled faces for the wild dataset and 994 subjects of the FERET database , with state-of-the-art comparisons indicating the supremacy of the authors ’ approach .
This paper summarizes the various components of face recognition research conducted at the IRIS Lab . First , fusion of visual and thermal infrared ( IR ) images for robust face recognition is discussed . Two techniques are implemented : data fusion and decision fusion .
Abstract In this paper , we consider the robust face recognition problem via matrix regression-based classification with block-norm(MRC-block ) . Specifically , we propose an inner-class sparse representation classification approach in which images are expressed as matrices instead of vectors and adopted to encode more discriminative information than other regression-based methods .
Systems.- Toward a Unified Probabilistic Framework for Object Recognition and Segmentation.- Distributed Multi-camera Surveillance for Aircraft Servicing Operations.- Mining Paths of Complex Crowd Scenes.- Geometric and Photometric Analysis for Interactively Recognizing Multicolor or Partially Occluded Objects.- A Three-Level Graph Based Interactive Volume Segmentation System.- Self-organizing Deformable Model : A New Method for Fitting Mesh Model to Given Object Surface.- Image-Based Deformation of Objects in Real Scenes.- Comparing Sphere-Tree Generators and Hierarchy Updates for Deformable Objects Collision Detection.- Simulating Complex Organ Interactions : Evaluation of a Soft Tissue Discrete Model.- Face Verification in Polar Frequency Domain : A Biologically Motivated Approach.- Face Alignment and Adaptive Weight Assignment for Robust Face Recognition.- Face Detection in Low-Resolution Images.- Investigating the Impact of Face Categorization on Recognition Performance.- A Novel Approach on Silhouette Based Human Motion Analysis for Gait Recognition.- A Hybrid HMM/DPA Adaptive Gesture Recognition Method.- Hifocon : Object and Dimensional Coherence and Correlation in Multidimensional Visualization.- Efficient Compression of Visibility Sets.- Rendering Optimizations Guided by Head-Pose Estimates and Their Uncertainty.- Acceptance of Visual Search Interfaces for the Web - Design and Empirical Evaluation of a Book Search Interface.- Distributed and Collaborative Biomedical Data Exploration.- Image Database Navigation : A Globe-Al Approach.- Viewpoint Interpolation Using an Ellipsoid Head Model for Video Teleconferencing.- Real-Time Video Annotations for Augmented Reality.- A Tree-Structured Model of Visual Appearance Applied to Gaze Tracking.- Emotional Expression in Virtual Agents Through Body Language.- Visual Tracking for Seamless 3D Interactions in Augmented Reality.- ARISupport -s ETDRS.- Using Multimodal MR Data for Segmentation and Topology Recovery of the Cerebral Superficial Venous Tree.- Loop Removal from Colon Central Path Through Skeleton Scale-Space Tracking.- Multiscale Segmentation of HRCT Images Using Bipolar Incoherent Filtering.- Building Statistical Atlas of White Matter Fiber Tract Based on Vector/Tensor Field Reconstruction in Diffusion Tensor MRI.- Interactive 3D Heart Chamber Partitioning with a New Marker-Controlled Watershed Algorithm.- Inferring Cause/Effect Relationships in Multi-sensor Ambient Intelligence Systems.- Toward a Unified Probabilistic Framework for Object Recognition and Segmentation.- Distributed Multi-camera Surveillance for Aircraft Servicing Operations.- Mining Paths of Complex Crowd Scenes.- Geometric and Photometric Analysis for Interactively Recognizing Multicolor or Partially Occluded Objects.- A Three-Level Graph Based Interactive Volume Segmentation System.- Self-organizing Deformable Model : A New Method for Fitting Mesh Model to Given Object Surface.- Image-Based Deformation of Objects in Real Scenes.- Comparing Sphere-Tree Generators and Hierarchy Updates for Deformable Objects Collision Detection.- Simulating Complex Organ Interactions : Evaluation of a Soft Tissue Discrete Model.- Face Verification in Polar Frequency Domain : A Biologically Motivated Approach.- Face Alignment and Adaptive Weight Assignment for Robust Face Recognition.- Face Detection in Low-Resolution Images.- Investigating the Impact of Face Categorization on Recognition Performance.- A Novel Approach on Silhouette Based Human Motion Analysis for Gait Recognition.- A Hybrid HMM/DPA Adaptive Gesture Recognition Method.- Hifocon : Object and Dimensional Coherence and Correlation in Multidimensional Visualization.- Efficient Compression of Visibility Sets.- Rendering Optimizations Guided by Head-Pose Estimates and Their Uncertainty.- Acceptance of Visual Search Interfaces for the Web - Design and Empirical Evaluation of a Book Search Interface.- Distributed and Collaborative Biomedical Data Exploration.- Image Database Navigation : A Globe-Al Approach.- Viewpoint Interpolation Using an Ellipsoid Head Model for Video Teleconferencing.- Real-Time Video Annotations for Augmented Reality.- A Tree-Structured Model of Visual Appearance Applied to Gaze Tracking.- Emotional Expression in Virtual Agents Through Body Language.- Visual Tracking for Seamless 3D Interactions in Augmented Reality.- ARISupport - Interaction Support for Augmented Reality Systems.- Background Updating for Visual Surveillance.- Pattern Discovery for Video Surveillance.- Real-Time Crowd Density Estimation Using Images.- Automatic Robust Background Modeling Using Multivariate Non-parametric Kernel Density Estimation for Visual Surveillance.- Recognition of Complex Human Behaviors in Pool Environment Using Foreground Silhouette.- Adaptive Background Subtraction with Multiple Feedbacks for Video Surveillance.- A Vectorial Self-dual Morphological Filter Based on Total Variation Minimization.-
However , portraits are subject to several complexities such as aesthetic sensibilities of the artist or social standing of the sitter . Thus , for robust automated face recognition , it becomes important to model the characteristics of the artist . In this paper , we focus on modeling the styles of artists by considering case studies involving Renaissance art-works .
AbstractAutomatic human face recognition is already in research from some decades due to its application in different fields . But there is no unique technique that is very much worthwhile for robust automatic human face recognition , suitable for all possible situations . In this paper , a new technique is proposed , which is a holistic approach , and it is based on ‘ one to all ’ comparison method .
A multiple observation-based scheme ( MObS ) is described for robust facial recognition , and a novel object recognition method called kernel mutual subspace method ( KMS ) is proposed . The mutual sub-space method ( MSM ) proposed by ( Maeda , et al. , 1999 ) is a powerful method for recognizing facial images .
Robust human face recognition is one of the most important open tasks in computer vision . This study deals with a challenging subproblem of face recognition : the aim of the paper is to give a precise estimation for the 3D head pose .
We describe a project to trial and develop enhanced surveillance technologies for public safety . A key technology is robust recognition of faces from low-resolution CCTV footage where there may be as few as 12 pixels between the eyes . Current commercial face recognition systems require 60 - 90 pixels between the eyes as well as tightly controlled image capture conditions .
Robust vision-based face recognition is one of most challenging tasks for robots . Recently the sparse representation-based classification ( SRC ) has been proposed to solve the problem .
These representations have shown strong performance for recognition of imagery and video in - the - wild in unconstrained datasets , with recent approaches demonstrating capabilities that exceed human performance on the well known Labeled Faces in the Wild dataset . The problem of face recognition may be described in terms of face verification and face identification . Face verification involves computing a one - to - one similarity between a probe image and a reference image , to determine if two image observations are of the same subject .
Similarly , the YouTubeFaces dataset contains 3425 videos of 1595 subjects , and compares pairs of videos of subjects for verification . These datasets have set the established standard for face recognition research , with steadily increasing performance . Recently , protocols for face identification have been introduced for LFW to address the performance evaluation for identification on a common dataset .
Figure 2 . Face images in the IJB - A dataset , sorted by their scores ( values of e in Eq . 2 ) from a single attention block trained in the face recognition task . The faces in the top , middle and bottom rows are sampled from the faces with scores in the highest 5 % , a 10 % window centered at the median , and the lowest 5 % , respectively .
However , it can not guarantee the identity by itself . To further enhance the identity keeping property , we add constrains on the feature representation extracted for face recognition or verification . In face verification task , two faces are from the same person when the distance between two features are smaller than ceratain threshold .
In cognitive science , there are a lot of studies on analyzing the mechanisms of face processing of neurons in visual cortex . Inspired by those works , we analyze the behaviours of neurons in artificial neural networks in a attempt to explain face recognition process in deep nets , what information is encoded in neurons , and how robust they are to corruptions . Our study is based on a high - performance deep convolutional neural network ( deep ConvNet ) , referred to as DeepID2 + , proposed in this paper .
document : Robust Face Detection via Learning Small Faces on Hard Images Recent anchor - based deep face detectors have achieved promising performance , but they are still struggling to detect hard faces , such as small , blurred and partially occluded faces .
This will calculate the Euclidean distance of the both the query and the dataset image . To investigate the effectiveness of different attributes and vital factors essential for face retrieval . By further enhancing , the accuracy of image retrieval can be improved .
These techniques are implemented and their performances are investigated using f rontal facial images from the ORL database . The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image retrieval , it still encounters problems especially in implementation ; e.g. Floating point operation and decomposition speed . We use the advantages of lifting scheme , a spatial approach for constructing wavelet filters , which provides feasible alternative for problems facing its classical counterpart .
Face image retrieval systems have attained much importance in recent times , due to many real time application demands it . Normally , image retrieval has become a challenging issue in the real world applications because the image collections are often high dimensional .
This process of retrieving the images from the big images dataset is called as content based image retrieval ( CBIR ) . These CBIR techniques are adopted recently in many image based applications like fingerprint matching image retrieval , face based image retrieval , and attribute based image retrieval . The area of interest during this review paper is face based image retrieval .
The system achieved good clustering accuracy when tested on different image sets and by different users . Moreover , features such as adding new images , face cluster navigation and face based image retrieval are added that greatly improve the usability of the system . It also facilitates efficient manual manipulations of clustering results .
Mining web facial images on the Internet has emerged as a promising paradigm towards auto face annotation . Therefore , a search assistance tool has been proposed , which helps the face image search based on annotation more efficient . In a face image database which has several face images of many personalities and the labels are not placed properly , which means some images are without label and some are without proper label , in this situation annotation based search will fail .
The similarities found from soft biometrics can also be useful as a source of evidence in courts of law because they are more descriptive than the numerical matching scores generated by a traditional face matcher . We propose to utilize demographic information ( e.g. , gender and ethnicity ) and facial marks ( e.g. , scars , moles , and freckles ) for improving face image matching and retrieval performance . An automatic facial mark detection method has been developed that uses ( 1 ) the active appearance model for locating primary facial features ( e.g. , eyes , nose , and mouth ) , ( 2 ) the Laplacian-of-Gaussian blob detection , and ( 3 ) morphological operators .
Linear Discriminant Analysis ( LDA ) is a popular feature extraction technique for face image recognition and retrieval . However , It often suffers from the small sample size problem when dealing with the high dimensional face data .
The feature vectors from the face image database are generated by using Gabor wavelet ( GW ) , wavelet Transformation ( WT ) , and Principal Component Analysis ( PCA ) . For the face image retrieval purpose , Artificial Neural Network ( ANN ) is adopted and its performance on the retrieval process is evaluated with PCA , WT , GW and their fusion as a feature vector . The query image is recognized from the faces returned by retrieval process by using Support Vector Machine ( SVM ) .
The apparatus for registering face identification features can eliminate time and effort for manually retrieving and preparing face images by being provided with a face image retrieving portion 11 for retrieving a face image of a person via a network using the person 's name as a keyword , a face feature extracting portion 12 for extracting features , which greatly influence identification of a person , from the face images retrieved by the face image retrieving portion 11 , and a celebrity name , face image and feature database 13 for registering the face images retrieved by the face image retrieving portion 11 and the face features extracted by the face feature extracting portion 12 in a state where they are associated with the person 's names .
Face image-video retrieval refers to retrieving videos of a specific person with image query or searching face images of one person by using a video clip query . It has attracted much attention for broad applications like suspect tracking and identifying .
We present the adaptive manifold self-organising map ( AMSOM ) for a face retrieval system . Our experimental results show that it has an excellent potential for face retrieval applications . As compared to the more traditional sub-space self-organising map , the results in many cases are better .
In this paper , we propose a novel bagging null space locality preserving discriminant analysis ( bagNLPDA ) method for facial feature extraction and recognition . The bagNLPDA method first projects all the training samples into the range space of a so-called locality preserving total scatter matrix without losing any discriminative information .
For image classification tasks , deep convolutional neural networks have achieved state-of-the-art results , and many activation functions have been proposed to enhance the classification performance of these networks . We explore the performance of multiple maxout activation variants on image classification , facial recognition and verification tasks using convolutional neural networks . Our experiments compare rectified linear unit , leaky rectified linear unit , scaled exponential linear unit , and hyperbolic tangent to four maxout variants .
Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision Fusion of visual, thermal, and range as a solution to illumination and pose restrictions in face recognition Matrix regression-based classification with block-norm Advances in Visual Computing, First International Symposium, ISVC 2005, Lake Tahoe, NV, USA, December 5-7, 2005, Proceedings Quantitative modeling of artist styles in Renaissance face portraiture Fuzzy matching of edge and curvature based features from range images for 3D face recognition Kernel mutual subspace method for robust facial image recognition Precise 3D pose estimation of human faces On intelligent surveillance systems and face recognition for mass transport security An Improved Robust Sparse Coding for Face Recognition with Disguise Template adaptation for face verification and identification Neural Aggregation Network for Video Face Recognition Deep Identity-aware Transfer of Facial Attributes Deeply learned face representations are sparse, selective, and robust Robust Face Detection via Learning Small Faces on Hard Images Enhancing Adaptive Content Based Face Image Retrieval in Database Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image Retrieval Combination of Dimensionality Reduction Techniques for Face Image Retrieval: A review IMAGE RETRIEVAL AND FACE RECOGNITION TECHNIQUES: LITERATURE SURVEY Face based image navigation and search Facial Image Annotation Search using CBIRand Supervised Label Refinement Face Matching and Retrieval Using Soft Biometrics Ensemble LDA for Face Recognition Content based Feature Combination Method for Face Image Retrieval using Neural Network and SVM Classifier for Face Recognition Face identifying feature registration device, a face identifying feature registration method, a face identifying feature registration program and a recording medium Implicit relative attribute enabled cross-modality hashing for face image-video retrieval Retrieving faces using adaptive subspace self-organising map Bagging null space locality preserving discriminant classifiers for face recognition Deep Learning with Maxout Activations for Visual Recognition and Verification
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HIP@ICDAR KES VISAPP (3) ICARCV Image Vis. Comput. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) ACM Multimedia ICB IRI
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