关键词: 人脸表情, 非特定人, 多分类器集成, 高阶奇异值分解, 模糊规则

Abstract: To the problem that the average recognition rate of person-independent facial expression is not high (about 65%), a new method of facial expression recognition, based on expression subspace and multi-classifiers integration, was proposed. In the training set 1, the features of global face region, eyes (include eyebrows) region and mouth region were respectively extracted and decomposed by Local Binary Pattern (LBP) and Higher Order Singular Value Decomposition (HOSVD), and the corresponding expression subspaces were built. Then the facial images of the training set 2 were trained by Support Vector Machine (SVM) in the expression subspaces and the parameters of fuzzy rule system were conducted. Finally, the expression subspaces and the multi-classifiers ensemble were combined to classify the expressions in test set. The experiments were conducted on JAFFE database and the average recognition rate was 71.43%. The experimental results show that the proposed method effectively reduces the influence caused by facial shape feature and facial expression manner, and it has better recognition rate.

Key words: facial expression, person-independent, multi-classifiers integration, Higher Order Singular Value Decomposition (HOSVD), fuzzy rule