Face Recognition Using Sparse Coding by Embedding Maximum Block Similarity
WANG Shu-Xian1, XIONG Cheng-Yi1, GAO Zhi-Rong2, ZHOU Cheng1, HOU Jian-Hua1
1.Hubei Key Laboratory of Intelligent Wireless Communication, College of Electronics and Information Engineering, South-Central University for Nationalities, Wuhan 430074
2.College of Computer Science, South-Central University for Nationalities, Wuhan 430074
The performance of sparse representation based face recognition (SRFC) can be effectively improved by embedding a priori of similarity information. Aiming at expressions variations, partial occlusions and disguise in the uncontrolled face images, SRFC by embedding maximum block similarity information is proposed. Firstly, the training samples and query samples are divided into multiple non-overlapping blocks in the same way. Secondly, the similarities of corresponding blocks between the query samples and the training samples are calculated. Then, the maximum value is extracted to measure the similarity of inter-images. Finally, the extracted maximum block similarity information is embedded into sparse representation stage. Experimental results on AR face databases show that the proposed method achieves better recognition performance compared with those based on embedding global similarity, especially when both training images and query images contain expression, occlusions or disguise.
[1] Zhao W Y, Chellappa R, Phillips P J, et al. Face Recognition: A Literature Survey. ACM Computing Surveys, 2003, 35(4): 399-458 [2] Zhou J, Lu C Y, Zhang C S, et al. A Survey of Automatic Human Face Recognition. Acta Electronica Sinica, 2000, 28(4): 102-106 (in Chinese) (周 杰,卢春雨,张长水,等.人脸自动识别方法综述.电子学报, 2000, 28(4): 102-106) [3] Hua G, Yang M H, Learned-Miller E, et al. Introduction to the Special Section on Real-World Face Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(10): 1921-1924 [4] Candes E J, Wakin M B. An Introduction to Compressive Sampling. IEEE Signal Processing Magazine, 2008, 25(2): 21-30 [5] 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 [6] Yang M, Zhang L. Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary // Proc of the 11th European Conference on Computer Vision. Crete, Greece, 2010, VI: 448-461 [7] Zhang L, Yang M, Feng X C. Sparse Representation or Collaborative Representation: Which Helps Face Recognition? // Proc of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011: 471-478 [8] Yang A Y, Wright J, Ma Y, et al. Feature Selection in Face Recognition: A Sparse Representation Perspective[EB/OL]. [2013-09-01]. http://perception.csl.illinois.edu/recognition/Files/PAMI_Feature.pdf [9] Lai J, Jiang X D. Modular Weighted Global Sparse Representation for Robust Face Recognition. IEEE Signal Processing Letters, 2012, 19(9): 571-574 [10] Deng W H, Hu J N, Guo J. Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary. IEEE Trans on Pa- ttern Analysis and Machine Intelligence, 2012, 34(9): 1864-1870 [11] Hui K H, Li C L, Zhang L. Sparse Neighbor Representation for Classification. Pattern Recognition Letters, 2012, 33(5): 661-669 [12] Lu C Y, Min H, Gui J, et al. Face Recognition via Weighted Sparse Representation. Journal of Visual Communication and Image Representation, 2013, 24(2): 111-116 [13] Yang M, Zhang L, Yang J, et al. Robust Sparse Coding for Face Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 625-632 [14] Wang J J, Yang J C, Yu K, et al. Locality-Constrained Linear Coding for Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 3360-3367 [15] Chao Y W, Yeh Y R, Chen Y W, et al. Locality-Constrained Group Sparse Representation for Robust Face Recognition // Proc of the 18th IEEE International Conference on Image Processing. Brussels, Belgium, 2011: 761-764 [16] Guo S, Ruan Q Q, Miao Z J. Similarity Weighted Sparse Representation for Classification // Proc of the 21st International Conference on Pattern Recognition. Tsukuba, Japan, 2012: 1241-1244 [17] Timofte R, van Gool L. Weighted Collaborative Representation and Classification of Images // Proc of the 21st International Conference on Pattern Recognition. Tsukuba, Japan, 2012: 1606-1610 [18] Waqas J, Zhang Y, Zhang L. Collaborative Neighbor Representation Based Classification Using l2-Minimization Approach. Pattern Recognition Letters, 2013, 34(2): 201-208 [19] Hu Z P, Li J. Face Recognition of Joint Sparse Representation Based on Low-Rank Subspace Recovery. Acta Electronica Sinica, 2013, 41(5): 987-991 (in Chinese) (胡正平,李 静.基于低秩子空间恢复的联合稀疏表示人脸识别算法.电子学报, 2013, 41(5): 987-991)
[20] Zhu J, Yang W K, Tang Z M. A Dictionary Learning Based Kernel Sparse Representation Method for Face Recognition. Pattern Recognition and Artificial Intelligence, 2012, 25(5): 859-864 (in Chinese) (朱 杰,杨万扣,唐振民.基于字典学习的核稀疏表示人脸识别方法.模式识别与人工智能, 2012, 25(5): 859-864) [21] Yu K, Zhang T, Gong Y. Nonlinear Learning Using Local Coordinate Coding // Proc of the 23rd Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2009: 2223-2231 [22] Yu K, Zhang T. Improved Local Coordinate Coding Using Local Tangents // Proc of the 27th International Conference on Machine Learning. Haifa, Israel, 2010: 1215-1222
2014, Vol. 27 
