Sparse tensor canonical correlation analysis for micro-expression recognition

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• 作者：Su-Jing Wang^a ; ^{wangsujing@psych.ac.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Wen-Jing Yan^bAuthor Vitae ; Tingkai Sun^cAuthor Vitae ; Guoying Zhao^dAuthor Vitae ; Xiaolan Fu^eAuthor Vitae
• 关键词：Micro-expression recognition ; Correlation analysis ; Sparse representation ; Tensor subspace
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：19 November 2016
• 年：2016
• 卷：214
• 期：Complete
• 页码：218-232
• 全文大小：2100 K

文摘

A micro-expression is considered a fast facial movement that indicates genuine emotions and thus provides a cue for deception detection. Due to its promising applications in various fields, psychologists and computer scientists, particularly those focus on computer vision and pattern recognition, have shown interest and conducted research on this topic. However, micro-expression recognition accuracy is still low. To improve the accuracy of such recognition, in this study, micro-expression data and their corresponding Local Binary Pattern (LBP) (Ojala et al., 2002) [1] code data are fused by correlation analysis. Here, we propose Sparse Tensor Canonical Correlation Analysis (STCCA) for micro-expression characteristics. A sparse solution is obtained by the regularized low rank matrix approximation. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that STCCA performs better than the Three-dimensional Canonical Correlation Analysis (3D-CCA) without sparse resolution. The experimental results also show that STCCA performs better than three-order Discriminant Tensor Subspace Analysis (DTSA3) with discriminant information, smaller projected dimensions and a larger training set sample size. The experiments also showed that Multi-linear Principal Component Analysis (MPCA) is not suitable for micro-expression recognition because the eigenvectors corresponding to smaller eigenvectors are discarded, and those eigenvectors include brief and subtle motion information.