The Experts below are selected from a list of 108180 Experts worldwide ranked by ideXlab platform
Guoying Zhao - One of the best experts on this subject based on the ideXlab platform.
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discriminative spatiotemporal local binary pattern with revisited integral projection for spontaneous facial micro Expression Recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Matti PietikäinenAbstract:Recently, there have been increasing interests in inferring mirco-Expression from facial image sequences. Due to subtle facial movement of micro-Expressions, feature extraction has become an important and critical issue for spontaneous facial micro-Expression Recognition. Recent works used spatiotemporal local binary pattern (STLBP) for micro-Expression Recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-Expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-Expression Recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an integral projection to resolve the problems of STLBP for micro-Expression Recognition. First, we revisit an integral projection for preserving the shape attribute of micro-Expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with local binary pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-Expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-Expression Recognition. Intensive experiments are conducted on three availably published micro-Expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-Expression Recognition.
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learning a target sample re generator for cross database micro Expression Recognition
ACM Multimedia, 2017Co-Authors: Yuan Zong, Xiaohua Huang, Wenming Zheng, Zhen Cui, Guoying ZhaoAbstract:In this paper, we investigate the cross-database micro-Expression Recognition problem, where the training and testing samples are from two different micro-Expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-Expression Recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-Expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-Expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-Expression Recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion Recognition methods, the proposed TSRG achieves more promising results.
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spontaneous facial micro Expression Recognition using discriminative spatiotemporal local binary pattern with an improved integral projection
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Xin Liu, Matti PietikäinenAbstract:Recently, there are increasing interests in inferring mirco-Expression from facial image sequences. Due to subtle facial movement of micro-Expressions, feature extraction has become an important and critical issue for spontaneous facial micro-Expression Recognition. Recent works usually used spatiotemporal local binary pattern for micro-Expression analysis. However, the commonly used spatiotemporal local binary pattern considers dynamic texture information to represent face images while misses the shape attribute of face images. On the other hand, their works extracted the spatiotemporal features from the global face regions, which ignore the discriminative information between two micro-Expression classes. The above-mentioned problems seriously limit the application of spatiotemporal local binary pattern on micro-Expression Recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an improved integral projection to resolve the problems of spatiotemporal local binary pattern for micro-Expression Recognition. Firstly, we develop an improved integral projection for preserving the shape attribute of micro-Expressions. Furthermore, an improved integral projection is incorporated with local binary pattern operators across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-Expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-Expression Recognition. Intensive experiments are conducted on three availably published micro-Expression databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-Expression Recognition.
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facial micro Expression Recognition using spatiotemporal local binary pattern with integral projection
International Conference on Computer Vision, 2015Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Matti PiteikainenAbstract:Recently, there are increasing interests in inferring mirco-Expression from facial image sequences. For micro-Expression Recognition, feature extraction is an important critical issue. In this paper, we proposes a novel framework based on a new spatiotemporal facial representation to analyze micro-Expressions with subtle facial movement. Firstly, an integral projection method based on difference images is utilized for obtaining horizontal and vertical projection, which can preserve the shape attributes of facial images and increase the discrimination for micro-Expressions. Furthermore, we employ the local binary pattern operators to extract the appearance and motion features on horizontal and vertical projections. Intensive experiments are conducted on three available published micro-Expression databases for evaluating the performance of the method. Experimental results demonstrate that the new spatiotemporal descriptor can achieve promising performance in micro-Expression Recognition.
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dynamic facial Expression Recognition using longitudinal facial Expression atlases
European Conference on Computer Vision, 2012Co-Authors: Yimo Guo, Guoying Zhao, Matti PietikäinenAbstract:In this paper, we propose a new scheme to formulate the dynamic facial Expression Recognition problem as a longitudinal atlases construction and deformable groupwise image registration problem. The main contributions of this method include: 1) We model human facial feature changes during the facial Expression process by a diffeomorphic image registration framework; 2) The subject-specific longitudinal change information of each facial Expression is captured by building an Expression growth model; 3) Longitudinal atlases of each facial Expression are constructed by performing groupwise registration among all the corresponding Expression image sequences of different subjects. The constructed atlases can reflect overall facial feature changes of each Expression among the population, and can suppress the bias due to inter-personal variations. The proposed method was extensively evaluated on the Cohn-Kanade, MMI, and Oulu-CASIA VIS dynamic facial Expression databases and was compared with several state-of-the-art facial Expression Recognition approaches. Experimental results demonstrate that our method consistently achieves the highest Recognition accuracies among other methods under comparison on all the databases.
Matti Pietikäinen - One of the best experts on this subject based on the ideXlab platform.
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discriminative spatiotemporal local binary pattern with revisited integral projection for spontaneous facial micro Expression Recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Matti PietikäinenAbstract:Recently, there have been increasing interests in inferring mirco-Expression from facial image sequences. Due to subtle facial movement of micro-Expressions, feature extraction has become an important and critical issue for spontaneous facial micro-Expression Recognition. Recent works used spatiotemporal local binary pattern (STLBP) for micro-Expression Recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-Expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-Expression Recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an integral projection to resolve the problems of STLBP for micro-Expression Recognition. First, we revisit an integral projection for preserving the shape attribute of micro-Expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with local binary pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-Expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-Expression Recognition. Intensive experiments are conducted on three availably published micro-Expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-Expression Recognition.
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spontaneous facial micro Expression Recognition using discriminative spatiotemporal local binary pattern with an improved integral projection
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Xin Liu, Matti PietikäinenAbstract:Recently, there are increasing interests in inferring mirco-Expression from facial image sequences. Due to subtle facial movement of micro-Expressions, feature extraction has become an important and critical issue for spontaneous facial micro-Expression Recognition. Recent works usually used spatiotemporal local binary pattern for micro-Expression analysis. However, the commonly used spatiotemporal local binary pattern considers dynamic texture information to represent face images while misses the shape attribute of face images. On the other hand, their works extracted the spatiotemporal features from the global face regions, which ignore the discriminative information between two micro-Expression classes. The above-mentioned problems seriously limit the application of spatiotemporal local binary pattern on micro-Expression Recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an improved integral projection to resolve the problems of spatiotemporal local binary pattern for micro-Expression Recognition. Firstly, we develop an improved integral projection for preserving the shape attribute of micro-Expressions. Furthermore, an improved integral projection is incorporated with local binary pattern operators across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-Expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-Expression Recognition. Intensive experiments are conducted on three availably published micro-Expression databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-Expression Recognition.
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dynamic facial Expression Recognition using longitudinal facial Expression atlases
European Conference on Computer Vision, 2012Co-Authors: Yimo Guo, Guoying Zhao, Matti PietikäinenAbstract:In this paper, we propose a new scheme to formulate the dynamic facial Expression Recognition problem as a longitudinal atlases construction and deformable groupwise image registration problem. The main contributions of this method include: 1) We model human facial feature changes during the facial Expression process by a diffeomorphic image registration framework; 2) The subject-specific longitudinal change information of each facial Expression is captured by building an Expression growth model; 3) Longitudinal atlases of each facial Expression are constructed by performing groupwise registration among all the corresponding Expression image sequences of different subjects. The constructed atlases can reflect overall facial feature changes of each Expression among the population, and can suppress the bias due to inter-personal variations. The proposed method was extensively evaluated on the Cohn-Kanade, MMI, and Oulu-CASIA VIS dynamic facial Expression databases and was compared with several state-of-the-art facial Expression Recognition approaches. Experimental results demonstrate that our method consistently achieves the highest Recognition accuracies among other methods under comparison on all the databases.
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facial Expression Recognition from near infrared videos
Image and Vision Computing, 2011Co-Authors: Guoying Zhao, Matti Taini, Stan Z Li, Xiaohua Huang, Matti PietikäinenAbstract:Facial Expression Recognition is to determine the emotional state of the face regardless of its identity. Most of the existing datasets for facial Expressions are captured in a visible light spectrum. However, the visible light (VIS) can change with time and location, causing significant variations in appearance and texture. In this paper, we present a novel research on a dynamic facial Expression Recognition, using near-infrared (NIR) video sequences and LBP-TOP (Local binary patterns from three orthogonal planes) feature descriptors. NIR imaging combined with LBP-TOP features provide an illumination invariant description of face video sequences. Appearance and motion features in slices are used for Expression classification, and for this, discriminative weights are learned from training examples. Furthermore, component-based facial features are presented to combine geometric and appearance information, providing an effective way for representing the facial Expressions. Experimental results of facial Expression Recognition using a novel Oulu-CASIA NIR&VIS facial Expression database, a support vector machine and sparse representation classifiers show good and robust results against illumination variations. This provides a baseline for future research on NIR-based facial Expression Recognition.
Xiaohua Huang - One of the best experts on this subject based on the ideXlab platform.
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discriminative spatiotemporal local binary pattern with revisited integral projection for spontaneous facial micro Expression Recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Matti PietikäinenAbstract:Recently, there have been increasing interests in inferring mirco-Expression from facial image sequences. Due to subtle facial movement of micro-Expressions, feature extraction has become an important and critical issue for spontaneous facial micro-Expression Recognition. Recent works used spatiotemporal local binary pattern (STLBP) for micro-Expression Recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-Expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-Expression Recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an integral projection to resolve the problems of STLBP for micro-Expression Recognition. First, we revisit an integral projection for preserving the shape attribute of micro-Expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with local binary pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-Expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-Expression Recognition. Intensive experiments are conducted on three availably published micro-Expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-Expression Recognition.
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learning a target sample re generator for cross database micro Expression Recognition
ACM Multimedia, 2017Co-Authors: Yuan Zong, Xiaohua Huang, Wenming Zheng, Zhen Cui, Guoying ZhaoAbstract:In this paper, we investigate the cross-database micro-Expression Recognition problem, where the training and testing samples are from two different micro-Expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-Expression Recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-Expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-Expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-Expression Recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion Recognition methods, the proposed TSRG achieves more promising results.
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spontaneous facial micro Expression Recognition using discriminative spatiotemporal local binary pattern with an improved integral projection
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Xin Liu, Matti PietikäinenAbstract:Recently, there are increasing interests in inferring mirco-Expression from facial image sequences. Due to subtle facial movement of micro-Expressions, feature extraction has become an important and critical issue for spontaneous facial micro-Expression Recognition. Recent works usually used spatiotemporal local binary pattern for micro-Expression analysis. However, the commonly used spatiotemporal local binary pattern considers dynamic texture information to represent face images while misses the shape attribute of face images. On the other hand, their works extracted the spatiotemporal features from the global face regions, which ignore the discriminative information between two micro-Expression classes. The above-mentioned problems seriously limit the application of spatiotemporal local binary pattern on micro-Expression Recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an improved integral projection to resolve the problems of spatiotemporal local binary pattern for micro-Expression Recognition. Firstly, we develop an improved integral projection for preserving the shape attribute of micro-Expressions. Furthermore, an improved integral projection is incorporated with local binary pattern operators across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-Expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-Expression Recognition. Intensive experiments are conducted on three availably published micro-Expression databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-Expression Recognition.
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facial micro Expression Recognition using spatiotemporal local binary pattern with integral projection
International Conference on Computer Vision, 2015Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Matti PiteikainenAbstract:Recently, there are increasing interests in inferring mirco-Expression from facial image sequences. For micro-Expression Recognition, feature extraction is an important critical issue. In this paper, we proposes a novel framework based on a new spatiotemporal facial representation to analyze micro-Expressions with subtle facial movement. Firstly, an integral projection method based on difference images is utilized for obtaining horizontal and vertical projection, which can preserve the shape attributes of facial images and increase the discrimination for micro-Expressions. Furthermore, we employ the local binary pattern operators to extract the appearance and motion features on horizontal and vertical projections. Intensive experiments are conducted on three available published micro-Expression databases for evaluating the performance of the method. Experimental results demonstrate that the new spatiotemporal descriptor can achieve promising performance in micro-Expression Recognition.
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facial Expression Recognition from near infrared videos
Image and Vision Computing, 2011Co-Authors: Guoying Zhao, Matti Taini, Stan Z Li, Xiaohua Huang, Matti PietikäinenAbstract:Facial Expression Recognition is to determine the emotional state of the face regardless of its identity. Most of the existing datasets for facial Expressions are captured in a visible light spectrum. However, the visible light (VIS) can change with time and location, causing significant variations in appearance and texture. In this paper, we present a novel research on a dynamic facial Expression Recognition, using near-infrared (NIR) video sequences and LBP-TOP (Local binary patterns from three orthogonal planes) feature descriptors. NIR imaging combined with LBP-TOP features provide an illumination invariant description of face video sequences. Appearance and motion features in slices are used for Expression classification, and for this, discriminative weights are learned from training examples. Furthermore, component-based facial features are presented to combine geometric and appearance information, providing an effective way for representing the facial Expressions. Experimental results of facial Expression Recognition using a novel Oulu-CASIA NIR&VIS facial Expression database, a support vector machine and sparse representation classifiers show good and robust results against illumination variations. This provides a baseline for future research on NIR-based facial Expression Recognition.
Sujing Wang - One of the best experts on this subject based on the ideXlab platform.
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discriminative spatiotemporal local binary pattern with revisited integral projection for spontaneous facial micro Expression Recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Matti PietikäinenAbstract:Recently, there have been increasing interests in inferring mirco-Expression from facial image sequences. Due to subtle facial movement of micro-Expressions, feature extraction has become an important and critical issue for spontaneous facial micro-Expression Recognition. Recent works used spatiotemporal local binary pattern (STLBP) for micro-Expression Recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-Expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-Expression Recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an integral projection to resolve the problems of STLBP for micro-Expression Recognition. First, we revisit an integral projection for preserving the shape attribute of micro-Expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with local binary pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-Expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-Expression Recognition. Intensive experiments are conducted on three availably published micro-Expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-Expression Recognition.
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spontaneous facial micro Expression Recognition using discriminative spatiotemporal local binary pattern with an improved integral projection
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Xin Liu, Matti PietikäinenAbstract:Recently, there are increasing interests in inferring mirco-Expression from facial image sequences. Due to subtle facial movement of micro-Expressions, feature extraction has become an important and critical issue for spontaneous facial micro-Expression Recognition. Recent works usually used spatiotemporal local binary pattern for micro-Expression analysis. However, the commonly used spatiotemporal local binary pattern considers dynamic texture information to represent face images while misses the shape attribute of face images. On the other hand, their works extracted the spatiotemporal features from the global face regions, which ignore the discriminative information between two micro-Expression classes. The above-mentioned problems seriously limit the application of spatiotemporal local binary pattern on micro-Expression Recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an improved integral projection to resolve the problems of spatiotemporal local binary pattern for micro-Expression Recognition. Firstly, we develop an improved integral projection for preserving the shape attribute of micro-Expressions. Furthermore, an improved integral projection is incorporated with local binary pattern operators across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-Expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-Expression Recognition. Intensive experiments are conducted on three availably published micro-Expression databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-Expression Recognition.
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facial micro Expression Recognition using spatiotemporal local binary pattern with integral projection
International Conference on Computer Vision, 2015Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Matti PiteikainenAbstract:Recently, there are increasing interests in inferring mirco-Expression from facial image sequences. For micro-Expression Recognition, feature extraction is an important critical issue. In this paper, we proposes a novel framework based on a new spatiotemporal facial representation to analyze micro-Expressions with subtle facial movement. Firstly, an integral projection method based on difference images is utilized for obtaining horizontal and vertical projection, which can preserve the shape attributes of facial images and increase the discrimination for micro-Expressions. Furthermore, we employ the local binary pattern operators to extract the appearance and motion features on horizontal and vertical projections. Intensive experiments are conducted on three available published micro-Expression databases for evaluating the performance of the method. Experimental results demonstrate that the new spatiotemporal descriptor can achieve promising performance in micro-Expression Recognition.
Wenming Zheng - One of the best experts on this subject based on the ideXlab platform.
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learning a target sample re generator for cross database micro Expression Recognition
ACM Multimedia, 2017Co-Authors: Yuan Zong, Xiaohua Huang, Wenming Zheng, Zhen Cui, Guoying ZhaoAbstract:In this paper, we investigate the cross-database micro-Expression Recognition problem, where the training and testing samples are from two different micro-Expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-Expression Recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-Expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-Expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-Expression Recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion Recognition methods, the proposed TSRG achieves more promising results.
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multi view facial Expression Recognition based on group sparse reduced rank regression
IEEE Transactions on Affective Computing, 2014Co-Authors: Wenming ZhengAbstract:In this paper, a novel multi-view facial Expression Recognition method is presented. Different from most of the facial Expression methods that use one view of facial feature vectors in the Expression Recognition, we synthesize multi-view facial feature vectors and combine them to this goal. In the facial feature extraction, we use the grids with multi-scale sizes to partition each facial image into a set of sub regions and carry out the feature extraction in each sub region. To deal with the prediction of Expressions, we propose a novel group sparse reduced-rank regression (GSRRR) model to describe the relationship between the multi-view facial feature vectors and the corresponding Expression class label vectors. The group sparsity of GSRRR enables us to automatically select the optimal sub regions of a face that contribute most to the Expression Recognition. To solve the optimization problem of GSRRR, we propose an efficient algorithm using inexact augmented Lagrangian multiplier (ALM) approach. Finally, we conduct extensive experiments on both BU-3DFE and Multi-PIE facial Expression databases to evaluate the Recognition performance of the proposed method. The experimental results confirm better Recognition performance of the proposed method compared with the state of the art methods.
