Kernel Canonical Correlation Analysis with Sparse Representation for Facial Expression Recognition
ZHOU Xiao-Yan1,2 ,ZHENG Wen-Ming3,XIN Ming-Hai3
1. Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science Technology,Nanjing 210044 2.Jiangsu Technology Engineering Center of Meteorological Sensor Network,Nanjing University of Information Science Technology,Nanjing 210044 3.Research Center for Learning Science,Southeast University,Nanjing 210096
Abstract In facial expression recognition,the existences of image noises and the irrelevant image information to the expression changes usually influence the recognition accuracy. The traditional facial expression recognition method using kernel canonical correlation analysis (KCCA) is difficulty to solve this problem. To overcome this drawback,a kernel canonical correlation analysis with sparse representation (SKCCA) is proposed and applied to the facial expression recognition. The basic idea of the SKCCA method is to utilize the sparse representation approach to choose the spectral components of the facial feature matrix before modeling the correlation between facial feature matrix and the expression semantic feature matrix. Then,the expression recognition is carried out based on the correlation model. To demonstrate the superiority of the proposed method over the traditional KCCA method,extensive experiments are conducted on the JAFFE database and the experimental results confirm the effectiveness of the proposed method.
[1] Ekman P,Friesen W V. Pictures of Facial Affect. San Francisco,USA: Consulting Psychologists Press,1976 [2] Kotsia I,Pitas I. Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines. IEEE Trans on Image Processing,2007,16(1): 172-187 [3] Hotelling H. Relations between Two Sets of Variates. Biometrika,1936,28(3/4): 321-377 [4] Hou Shudong,Sun Quansen,Xia Deshen. Supervised Locality Preserving Canonical Correlation Analysis Algorithm. Pattern Recognition and Artificial Intelligence,2012,25 (1): 143-149 (in Chinese) (侯书东,孙权森,夏德深.一种监督的局部保持典型相关分析算法.模式识别与人工智能,2012,25(1): 143-149) [5] Hong Quan,Chen Songcan,Ni Xuelei,Sub-Pattern Canonical Correlation Analysis with Application in Face Recognition. Acta Automatica Sinica,2008,34(1): 21-30 (in Chinese) (洪 泉,陈松灿,倪雪蕾.子模式典型相关分析及其在人脸识别中的应用.自动化学报,2008,34(1): 21-30) [6] Peng Yan,Zhang Daoqiang. Semi-Supervised Canonical Correlation Analysis Algorithm. Journal of Software,2008,19(11): 2822-2832 (in Chinese) (彭 岩,张道强.半监督典型相关分析算法.软件学报,2008,19(11): 2822-2832) [7] Zheng W,Zhou X,Zou C,et al. Facial Expression Recognition Using Kernel Canonical Correlation Analysis. IEEE Trans on Neural Networks,2006,17(1): 233-238 [8] Hardoon D R,Shawe-Taylor J. Sparse Canonical Correlation Analysis. Machine Learning,2011,83(3): 331-353 [9] Lykou A,Whittaker J. Sparse CCA Using a Lasso with Positivity Constraint. Computational Statistics and Data Analysis,2010,54(12): 3144-3157 [10] Zhuang Ling,Zhuang Yueting,Wu Jiangqin,et al. Image Retrieval Approach Based on Sparse Canonical Correlation Analysis. Journal of Software,2012,23(5): 1295-1304 (in Chinese) (庄 凌,庄越挺,吴江琴,等.一种基于稀疏典型性相关分析的图像检索方法. 软件学报,2012,23(5): 1295-1304) [11] Hou Shudong,Sun Quansen. Sparsity Preserving Canonical Correlation Analysis with Application in Feature Fusion. Acta Automatica Sinica,2012,38(4): 659-665 (in Chinese) (侯书东,孙权森.稀疏保持典型相关分析及在特征融合中的应用.自动化学报,2012,38(4): 659-665) [12] Tibshirani R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B,1996,58(1): 267-288 [13] Shen H P,Huang J Z. Sparse Principle Component Analysis via Regularized Low Rank Matrix Approximation. Journal of Multi-variate Analysis,2008,99(6): 1015-1034 [14] Eckart C,Young G. The Approximation of One Matrix by Another of Low Rank. Psychometrika,1936,1(3): 211-218 [15] Zhang Z,Lyons M,Schuster M,et al. Comparison between Geometry-Based and Gabor Wavelets-Based Facial Expression Recognition Using Multi-layer Perception // Procs of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition. Nara,Japan,1998: 454-459 [16] Lyons M L,Budynek J,Akamatsu S. Automatic Classification of Single Facial Images. IEEE Trans on Pattern Analysis and Machine Intelligence,1999,21(12): 1357-1362 [17] Zheng Wenming,Tang Hao. Lin Zhouchen,et al. A Novel Approach to Expression Recognition from Non-frontal Face Images,// Proc of the 12th IEEE International Conference on Computer Vision. Kyoto,Japan,2009: 1901-1908 [18] Zhao Guoying,Pietikinen M. Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Trans on Pattern Analysis and Machine Intelligence,2007,29(6): 915-928
2013, Vol. 26 
