In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers...In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers in the experiment.In this paper,a simple attack method called incomplete physical adversarial attack(IPAA)is proposed to simulate physical attacks.Different from the process of physical attacks,when an IPAA is conducted,a photo of the adversarial sticker is embedded into a facial image as the input to attack FR systems,which can obtain results similar to those of physical attacks without inviting any volunteers.The results show that IPAA has a higher similarity with physical attacks than digital attacks,indicating that IPAA is able to evaluate the performance of physical attacks.IPAA is effective in quantitatively measuring the impact of the sticker location on the results of attacks.展开更多
Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extrac...Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.展开更多
We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to...We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.By applying the Mellin transform to computer-generated holograms and performing correlation between them,which,to the best of our knowledge,is being done for the first time,we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy.Digital holograms of 3D faces are generated from a face database,and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0.Within this range,the method achieves 100%recognition accuracy,as confirmed by both simulation-based and hybrid optical/digital experimental validations.Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition,as evidenced by the sharp correlation peaks and higher peak-to-noise ratio(PNR)values than that of using conventional holograms without the Mellin transform.Additionally,the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness,demonstrating its capability to accurately identify and distinguish 3D faces at various scales.This work provides a promising solution for advanced biometric systems,especially for those which require 3D scale-invariant recognition.展开更多
The lack of facial features caused by wearing masks degrades the performance of facial recognition systems.Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer a...The lack of facial features caused by wearing masks degrades the performance of facial recognition systems.Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer.Besides,previous research fails to consider the facial characteristics including occluded and unoccluded parts.To solve the above problems,we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion.Specifically,the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition.Then,a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial.Furthermore,a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load,computational power,bandwidth,and delay tolerance constraints of the edge.This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy.The experimental results show that the proposed method achieves an average gain of about 21%in recognition latency,while the accuracy of the face recognition task is basically the same compared to the baseline method.展开更多
In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algori...In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.展开更多
With the aim of extracting the features of face images in face recognition, a new method of face recognition by fusing global features and local features is presented. The global features are extracted using principal...With the aim of extracting the features of face images in face recognition, a new method of face recognition by fusing global features and local features is presented. The global features are extracted using principal component analysis （PCA）. Active appearance model （AAM） locates 58 facial fiducial points, from which 17 points are characterized as local features using the Gabor wavelet transform （GWT）. Normalized global match degree （local match degree） can be obtained by global features （local features） of the probe image and each gallery image. After the fusion of normalized global match degree and normalized local match degree, the recognition result is the class that included the gallery image corresponding to the largest fused match degree. The method is evaluated by the recognition rates over two face image databases （AR and SJTU-IPPR）. The experimental results show that the method outperforms PCA and elastic bunch graph matching （EBGM）. Moreover, it is effective and robust to expression, illumination and pose variation in some degree.展开更多
Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of sampl...Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.展开更多
Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample si...Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size （SSS） property of face recognition. To solve the two problems,local Bagging （L-Bagging） is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition. The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image. Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions,L-Bagging deals with SSS problem and generates more diverse component classifiers. Experimental results on four standard face image databases （AR,Yale,ORL and Yale B） indicate that the proposed L-Bagging method is effective and robust to illumination,occlusion and slight pose variation.展开更多
To improve the classification performance of the kernel minimum squared error（ KMSE）, an enhanced KMSE algorithm（ EKMSE） is proposed. It redefines the regular objective function by introducing a novel class label ...To improve the classification performance of the kernel minimum squared error（ KMSE）, an enhanced KMSE algorithm（ EKMSE） is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix.Compared with the common methods, the newobjective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE,some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification（ CRC）.展开更多
Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with ...Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-展开更多
Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. ...Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.展开更多
Face recognition provides a natural visual interface for human computer interaction （HCI） applications. The process of face recognition, however, is inhibited by variations in the appearance of face images caused by...Face recognition provides a natural visual interface for human computer interaction （HCI） applications. The process of face recognition, however, is inhibited by variations in the appearance of face images caused by changes in lighting, expression, viewpoint, aging and introduction of occlusion. Although various algorithms have been presented for face recognition, face recognition is still a very challenging topic. A novel approach of real time face recognition for HCI is proposed in the paper. In view of the limits of the popular approaches to foreground segmentation, wavelet multi-scale transform based background subtraction is developed to extract foreground objects. The optimal selection of the threshold is automatically determined, which does not require any complex supervised training or manual experimental calibration. A robust real time face recognition algorithm is presented, which combines the projection matrixes without iteration and kernel Fisher discriminant analysis （KFDA） to overcome some difficulties existing in the real face recognition. Superior performance of the proposed algorithm is demonstrated by comparing with other algorithms through experiments. The proposed algorithm can also be applied to the video image sequences of natural HCI.展开更多
Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classifi...Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features＇ contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis （ICA） features. These local features are acquired using locally lateral subspace （LLS） strategy. Then, through linear discriminant analysis （LDA） we investigate the intraclass and interclass representation of each local ICA feature and express each feature＇s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding （LLE） along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE, FERET and CK% is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects.展开更多
The purpose of this paper is to solve the problem of robust face recognition(FR) with single sample per person(SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation(LRSR) ...The purpose of this paper is to solve the problem of robust face recognition(FR) with single sample per person(SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation(LRSR) to tackle the problem of query images with various intra-class variations,e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images.The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intraclass variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.展开更多
A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional de...A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.展开更多
Face recognition systems have enhanced human-computer interactions in the last ten years.However,the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations.Pri...Face recognition systems have enhanced human-computer interactions in the last ten years.However,the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations.Principal Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis techniques.Compared to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data samples.Apart from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN.PCA-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s face.This study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face detection.The experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.展开更多
Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face r...Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace structures of data while sparsity helps to recognize the data class, the obtained test sample representations are both representative and discriminative. Using the representation vector of a test sample, LRSRC classifies the test sample into the class which generates minimal reconstruction error. Experimental results on several face image databases show the effectiveness and robustness of LRSRC in face image recognition.展开更多
It has been demonstrated that the linear discriminant analysis (LDA) is an effective approach in face recognition tasks. However, due to the high dimensionality of an image space, many LDA based approaches first use t...It has been demonstrated that the linear discriminant analysis (LDA) is an effective approach in face recognition tasks. However, due to the high dimensionality of an image space, many LDA based approaches first use the principal component analysis (PCA) to project an image into a lower dimensional space, then perform the LDA transform to extract discriminant feature. But some useful discriminant information to the following LDA transform will be lost in the PCA step. To overcome these defects, a face recognition method based on the discrete cosine transform (DCT) and the LDA is proposed. First the DCT is used to achieve dimension reduction, then LDA transform is performed on the lower space to extract features. Two face databases are used to test our method and the correct recognition rates of 97.5% and 96.0% are obtained respectively. The performance of the proposed method is compared with that of the PCA+ LDA method and the results show that the method proposed outperforms the PCA+ LDA method.展开更多
The 3D face recognition attracts more and more attention because of its insensitivity to the variance of illumination and pose.There are many crucial problems to be solved in this topic,such as 3D face representation ...The 3D face recognition attracts more and more attention because of its insensitivity to the variance of illumination and pose.There are many crucial problems to be solved in this topic,such as 3D face representation and effective multi-feature fusion.In this paper,a novel 3D face recognition algorithm is proposed and its performance is demonstrated on BJUT-3D face database.This algorithm chooses face surface property and the principle component of relative relation matrix as the face representation features.The similarity metric measure for each feature is defined.A feature fusion strategy is proposed.It is a linear weighted strategy based on Fisher linear discriminant analysis.Finally,the presented algorithm is tested on the BJUT-3D face database.It is concluded that the performance of the algorithm and fusion strategy is satisfying.展开更多
In order to distinguish faces of various angles during face recognition, an algorithm of the combination of approximate dynamic programming （ADP） called action dependent heuristic dynamic programming （ADHDP） and p...In order to distinguish faces of various angles during face recognition, an algorithm of the combination of approximate dynamic programming （ADP） called action dependent heuristic dynamic programming （ADHDP） and particle swarm optimization （PSO） is presented. ADP is used for dynamically changing the values of the PSO parameters. During the process of face recognition, the discrete cosine transformation （DCT） is first introduced to reduce negative effects. Then, Karhunen-Loeve （K-L） transformation can be used to compress images and decrease data dimensions. According to principal component analysis （PCA）, the main parts of vectors are extracted for data representation. Finally, radial basis function （RBF） neural network is trained to recognize various faces. The training of RBF neural network is exploited by ADP-PSO. In terms of ORL Face Database, the experimental result gives a clear view of its accurate efficiency.展开更多
文摘In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers in the experiment.In this paper,a simple attack method called incomplete physical adversarial attack(IPAA)is proposed to simulate physical attacks.Different from the process of physical attacks,when an IPAA is conducted,a photo of the adversarial sticker is embedded into a facial image as the input to attack FR systems,which can obtain results similar to those of physical attacks without inviting any volunteers.The results show that IPAA has a higher similarity with physical attacks than digital attacks,indicating that IPAA is able to evaluate the performance of physical attacks.IPAA is effective in quantitatively measuring the impact of the sticker location on the results of attacks.
基金the financial support from Natural Science Foundation of Gansu Province(Nos.22JR5RA217,22JR5RA216)Lanzhou Science and Technology Program(No.2022-2-111)+1 种基金Lanzhou University of Arts and Sciences School Innovation Fund Project(No.XJ2022000103)Lanzhou College of Arts and Sciences 2023 Talent Cultivation Quality Improvement Project(No.2023-ZL-jxzz-03)。
文摘Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.
基金financial supports from the National Natural Science Foundation of China(Grant No.6227511362405124).
文摘We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.By applying the Mellin transform to computer-generated holograms and performing correlation between them,which,to the best of our knowledge,is being done for the first time,we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy.Digital holograms of 3D faces are generated from a face database,and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0.Within this range,the method achieves 100%recognition accuracy,as confirmed by both simulation-based and hybrid optical/digital experimental validations.Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition,as evidenced by the sharp correlation peaks and higher peak-to-noise ratio(PNR)values than that of using conventional holograms without the Mellin transform.Additionally,the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness,demonstrating its capability to accurately identify and distinguish 3D faces at various scales.This work provides a promising solution for advanced biometric systems,especially for those which require 3D scale-invariant recognition.
基金supported by National Natural Science Foundation of China(61901071,61871062,61771082,U20A20157)Science and Natural Science Foundation of Chongqing,China(cstc2020jcyjzdxmX0024)+6 种基金University Innovation Research Group of Chongqing(CXQT20017)Program for Innovation Team Building at Institutions of Higher Education in Chongqing(CXTDX201601020)Natural Science Foundation of Chongqing,China(CSTB2022NSCQ-MSX0600)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)Chongqing Municipal Technology Innovation and Application Development Special Key Project(cstc2020jscxdxwtBX0053)China Postdoctoral Science Foundation Project,China(2022MD723723)Chongqing Postdoctoral Research Project Special Funding,China(2023CQBSHTB3092)。
文摘The lack of facial features caused by wearing masks degrades the performance of facial recognition systems.Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer.Besides,previous research fails to consider the facial characteristics including occluded and unoccluded parts.To solve the above problems,we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion.Specifically,the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition.Then,a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial.Furthermore,a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load,computational power,bandwidth,and delay tolerance constraints of the edge.This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy.The experimental results show that the proposed method achieves an average gain of about 21%in recognition latency,while the accuracy of the face recognition task is basically the same compared to the baseline method.
文摘In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.
文摘With the aim of extracting the features of face images in face recognition, a new method of face recognition by fusing global features and local features is presented. The global features are extracted using principal component analysis （PCA）. Active appearance model （AAM） locates 58 facial fiducial points, from which 17 points are characterized as local features using the Gabor wavelet transform （GWT）. Normalized global match degree （local match degree） can be obtained by global features （local features） of the probe image and each gallery image. After the fusion of normalized global match degree and normalized local match degree, the recognition result is the class that included the gallery image corresponding to the largest fused match degree. The method is evaluated by the recognition rates over two face image databases （AR and SJTU-IPPR）. The experimental results show that the method outperforms PCA and elastic bunch graph matching （EBGM）. Moreover, it is effective and robust to expression, illumination and pose variation in some degree.
文摘Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.
文摘Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size （SSS） property of face recognition. To solve the two problems,local Bagging （L-Bagging） is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition. The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image. Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions,L-Bagging deals with SSS problem and generates more diverse component classifiers. Experimental results on four standard face image databases （AR,Yale,ORL and Yale B） indicate that the proposed L-Bagging method is effective and robust to illumination,occlusion and slight pose variation.
基金The Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the National Natural Science Foundation of China(No.61572258,61103141,51405241)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20151530)Overseas Training Programs for Outstanding Young Scholars of Universities in Jiangsu Province
文摘To improve the classification performance of the kernel minimum squared error（ KMSE）, an enhanced KMSE algorithm（ EKMSE） is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix.Compared with the common methods, the newobjective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE,some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification（ CRC）.
基金This project was supported by Shanghai Shu Guang Project.
文摘Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.
基金supported by the National Natural Science Foundation of China (Grant No.60872117)the Leading Academic Discipline Project of Shanghai Municipal Education Commission (Grant No.J50104)
文摘Face recognition provides a natural visual interface for human computer interaction （HCI） applications. The process of face recognition, however, is inhibited by variations in the appearance of face images caused by changes in lighting, expression, viewpoint, aging and introduction of occlusion. Although various algorithms have been presented for face recognition, face recognition is still a very challenging topic. A novel approach of real time face recognition for HCI is proposed in the paper. In view of the limits of the popular approaches to foreground segmentation, wavelet multi-scale transform based background subtraction is developed to extract foreground objects. The optimal selection of the threshold is automatically determined, which does not require any complex supervised training or manual experimental calibration. A robust real time face recognition algorithm is presented, which combines the projection matrixes without iteration and kernel Fisher discriminant analysis （KFDA） to overcome some difficulties existing in the real face recognition. Superior performance of the proposed algorithm is demonstrated by comparing with other algorithms through experiments. The proposed algorithm can also be applied to the video image sequences of natural HCI.
基金supported by Ministry of Higher Education MalaysiaUniversiti Teknologi MARA,Malaysia
文摘Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features＇ contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis （ICA） features. These local features are acquired using locally lateral subspace （LLS） strategy. Then, through linear discriminant analysis （LDA） we investigate the intraclass and interclass representation of each local ICA feature and express each feature＇s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding （LLE） along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE, FERET and CK% is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects.
基金supported in part by the National Natural Science Foundation of China(61673402,61273270,60802069)the Natural Science Foundation of Guangdong Province(2017A030311029,2016B010109002,2015B090912001,2016B010123005,2017B090909005)+1 种基金the Science and Technology Program of Guangzhou of China(201704020180,201604020024)the Fundamental Research Funds for the Central Universities of China
文摘The purpose of this paper is to solve the problem of robust face recognition(FR) with single sample per person(SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation(LRSR) to tackle the problem of query images with various intra-class variations,e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images.The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intraclass variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.
基金National "863" project (2001AA114140) the National Natural Science Foundation of China (60135020).
文摘A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.
文摘Face recognition systems have enhanced human-computer interactions in the last ten years.However,the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations.Principal Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis techniques.Compared to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data samples.Apart from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN.PCA-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s face.This study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face detection.The experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.
基金supported by National Natural Science Foundation of China(No.61374134)the key Scientific Research Project of Universities in Henan Province,China(No.15A413009)
文摘Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace structures of data while sparsity helps to recognize the data class, the obtained test sample representations are both representative and discriminative. Using the representation vector of a test sample, LRSRC classifies the test sample into the class which generates minimal reconstruction error. Experimental results on several face image databases show the effectiveness and robustness of LRSRC in face image recognition.
文摘It has been demonstrated that the linear discriminant analysis (LDA) is an effective approach in face recognition tasks. However, due to the high dimensionality of an image space, many LDA based approaches first use the principal component analysis (PCA) to project an image into a lower dimensional space, then perform the LDA transform to extract discriminant feature. But some useful discriminant information to the following LDA transform will be lost in the PCA step. To overcome these defects, a face recognition method based on the discrete cosine transform (DCT) and the LDA is proposed. First the DCT is used to achieve dimension reduction, then LDA transform is performed on the lower space to extract features. Two face databases are used to test our method and the correct recognition rates of 97.5% and 96.0% are obtained respectively. The performance of the proposed method is compared with that of the PCA+ LDA method and the results show that the method proposed outperforms the PCA+ LDA method.
基金Supported by National Natural Science Foundation of China(60533030)Beijing Natural Science Foundation(4061001)
文摘The 3D face recognition attracts more and more attention because of its insensitivity to the variance of illumination and pose.There are many crucial problems to be solved in this topic,such as 3D face representation and effective multi-feature fusion.In this paper,a novel 3D face recognition algorithm is proposed and its performance is demonstrated on BJUT-3D face database.This algorithm chooses face surface property and the principle component of relative relation matrix as the face representation features.The similarity metric measure for each feature is defined.A feature fusion strategy is proposed.It is a linear weighted strategy based on Fisher linear discriminant analysis.Finally,the presented algorithm is tested on the BJUT-3D face database.It is concluded that the performance of the algorithm and fusion strategy is satisfying.
基金This work was supported by Natural Science Foundation of Huazhong University of Science and Technology of PRC(No.2007Q006B).
文摘In order to distinguish faces of various angles during face recognition, an algorithm of the combination of approximate dynamic programming （ADP） called action dependent heuristic dynamic programming （ADHDP） and particle swarm optimization （PSO） is presented. ADP is used for dynamically changing the values of the PSO parameters. During the process of face recognition, the discrete cosine transformation （DCT） is first introduced to reduce negative effects. Then, Karhunen-Loeve （K-L） transformation can be used to compress images and decrease data dimensions. According to principal component analysis （PCA）, the main parts of vectors are extracted for data representation. Finally, radial basis function （RBF） neural network is trained to recognize various faces. The training of RBF neural network is exploited by ADP-PSO. In terms of ORL Face Database, the experimental result gives a clear view of its accurate efficiency.
