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| Facial Expression Recognition Based on Modular Two Dimensional Canonical Correlation Analysis |
| Yanfeng Sun, Tianchang Jiang, Yongli Hu |
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Abstract Traditional canonical correlation analysis as an effective feature
extraction method has been widely used in many areas of pattern
recognition, including facial expression recognition. The canonical
correlation analysis makes the within-class covariance matrix
constituted of two groups of feature vectors to be singular,
resulting in small sample size problem, meanwhile the canonical
correlation analysis as a global linear projection method, not a
good description of nonlinear facial expression recognition problem.
In order to solve the above problems, we present a facial expression
recognition algorithm based on modular two dimensional canonical
correlation analysis. The algorithm uses a modular method, and
better takes advantage of local non-linear characteristics of the
facial expression image, meanwhile the direct use of two-dimensional
facial expression feature matrix is effective to relieve the small
sample size problem. In recognition stage, we use the voting method,
which can improve the robustness of local expression feature
changes. Experiments on JAFFE facial expression database show that
the proposed method is robust and effective.
DOI: 10.12733/jics20101689
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