Image Local Feature Extraction Method Based on Two-dimensional Partial Least Square and Its Application in Facial Expression Recognition
基于2维偏最小二乘法的图像局部特征提取及其在面部表情识别中的应用

In this paper,we proposed a local feature extraction method based on a two-dimensional partial least square(2DPLS) for images,and then applied it in the facial expression recognition.Firstly,this method combines texture features of all sub windows of an image extracted by local binary pattern(LBP) into a local texture feature matrix.In order to extract the discrimination information,the traditional PLS method is extended to 2DPLS method since the images have been transformed to local texture matrices.In the 2DPLS method,the class-membership matrix is modified to adapt to the matrix form of the samples and represents the difference of the importance of the local image information.Meanwhile,the analytic form of the generalized inverse of class-membership matrix is derived.The experiment results based on JAFFE database show that the proposed method can effectively extract the local feature from images and achieve good performance in facial expression recognition.