Facial Expression Recognition Based on Local Gabor Filter Bank and PCA + LDA
一种基于局部Gabor滤波器组及PCA＋LDA的人脸表情识别方法

This paper proposes a new local Gabor filter bank to overcome the disadvantage of the traditional Gabor filter bank,which needs a lot of time to extract Gabor feature vectors and the high-dimensional Gabor feature vectors are very redundant.In order to evaluate the performance of local Gabor filter bank,a Facial Expression Recognition(FER) system based on Gabor feature is presented.Firstly the FER system extracts Gabor feature of pure facial expression images after preprocess,then it uses a two-stage method PCA plus LDA to select and compress the sub-sampled Gabor feature,finally it adopts K nearest neighbor classifier to recognize facial expression.Experimental results show that the method is effective for both dimension reduction and recognition performance.The novelty of the method is to select partial Gabor filter bank with part of m scales and n orientations to extract Gabor feature.The best average recognition rate of 97.33% was achieved,which indicated this method was suit for facial expression analysis.