针对传统的Gabor滤波器组存在特征提取时间较长以及特征数据存在冗余性的缺点,提出了一种新颖的局部Gabor滤波器组。为了评估该方法的识别性能,提出了一个基于Gabor特征的人脸表情识别系统。该系统首先对经过预处理之后的纯表情图像提取Gabor特征,然后用PCA LDA方法对采样后的特征进行特征选择,最后采用K近邻分类方法识别人脸表情。实验结果表明,这种方法无论在计算量还是识别性能上都比传统的Gabor滤波器组更具有优势。该方法的创新之处在于选取局部Gabor滤波器,最高平均识别率达到了97.33%,表明其适合于人脸表情图像的分析。

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.