Abstract Considering the complementation of global and local components, bi-L1 sparse representation algorithm for face recognition based on fusion of global and separated components is proposed. Firstly, based on L1 sparse representation, the global information is used to obtain the global sparse approximation. Then, several slightly overlapping face components are extracted and aligned in the recognition model of separated components. After that, the sparse representation of all the components is obtained respectively. The sparse approximation results of each component are combined with a similarity voting method based on the residuals of class representation. Finally, the weighted integration of the global and components sparse representation is used to construct the bi-L1 sparse representation classifier in decision-making layer. The experimental results on public available database demonstrate that the performance of the integration classifier is superior to that of each single module. Due to the fusion of component information which is insensitive to variation of illumination and expression etc., the robustness of the system is enhanced.
HU Zheng-Ping,SONG Shu-Fen. Bi-L1 Sparse Representation Algorithm for Face Recognition Based on Fusion of Global and Separated Components[J]. , 2012, 25(2): 256-261.
[1] Wright J,Yang A Y,Ganesh A,et al.Robust Face Recognition via Sparse Representation.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(2): 210-227 [2] Huang Junzhou,Huang Xiaolei,Metaxas D.Simultaneous Image Transformation and Sparse Representation Recovery // Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Anchorage,USA,2008: 1-8 [3] Cheng Ping,Liu Haitian,Zhao Jiaqun.A Novel Sparse Representation Algorithm Based on Local Competitions // Proc of the IEEE International Conference on Information Theory and Information Security.Beijing,China,2010: 798-801 [4] He Ran,Hu Baogang,Zheng Weishi,et al.Two-Stage Sparse Representation for Robust Recognition on Large-Scale Database // Proc of the 24th AAAI Conference on Artificial Intelligence.Atlanta,USA,2010: 475-480 [5] Majumdar A,Ward R K.Fast Group Sparse Classification.Canadian Journal of Electrical and Computer Engineering,2009,34(4): 136-144 [6] Yang Meng,Zhang Lei.Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary // Proc of the 11th European Conference on Computer Vision.Heraklion,Greece,2010: 448-461 [7] Li Chunguang,Guo Jun,Zhang Honggang.Local Sparse Representation Based Classification // Proc of the 20th International Conference on Pattern Recognition.Istanbul,Turkey,2010: 649-652 [8] Zhang Nan,Yang Jian.K Nearest Neighbor Based Local Sparse Representation Classifier // Proc of the Chinese Conference on Pattern Recognition.Chongqing,China,2010: 400-404 [9] Huang Di,Ouji K,Ardabilian M,et al.3D Face Recognition Based on Local Shape Patterns and Sparse Representation Classifier // Proc of the 17th International Multimedia Modeling Conference.Taipei,China,2011: 206-216 [10] Cotter S F.Weighted Voting of Sparse Representation Classifiers for Facial Expression Recognition // Proc of the 18th European Signal Processing Conference.Aalborg,Denmark,2010: 1164-1168 [11] Cao Zhimin,Yin Qi,Tang Xiaoou,et al.Face Recognition with Learning-Based Descriptor // Proc of the IEEE Conference on Computer Vision and Pattern Recognition.San Francisco,USA,2010: 2707-2714 [12] Su Yu,Shan Shiguang,Chen Xilin,et al.Integration of Global and Local Feature for Face Recognition.Journal of Software,2010,21(8): 1849-1862 (in Chinese) (苏 煜,山世光,陈熙霖,等.基于全局和局部特征集成的人脸识别.软件学报,2010,21(8): 1849-1862)
2012, Vol. 25 
