Kernel principal component analysis (KPCA), introduced by Sch

In this paper, we address the facial expression recognition (FER) problem using kernel canonical correlation analysis (KCCA). Following the method proposed by Lyons et al. [7] and Zhang et al. [8], we locate 34 points manually from each facial image as the landmark locations and then convert these geometric locations into a labeled graph (LG) [7] vector using Gabor wavelet transformation method to represent the facial image. On the other hand, a semantic expression vector consisting of the semantic ratings of each facial image is used as the semantic expression representation. Learning the correlation between the LG vector and the semantic expression vector is performed by KCCA. According to this correlation, we can estimate the associated semantic expression vector of a given test image and then perform the expression classification according to this semantic expression vector. Moreover, we propose another efficient algorithm for KCCA, which can avoid regularization to the Gram matrix. The experimental results on the Japanese Female Facial Expression (JAFFE) database and the Ekman's "Pictures of Facial Affect" database illustrate effectiveness of the KCCA method in facial expression recognition problem.

随着信息和计算机技术的飞速发展，人脸面部表情识别技术越来越受到重视。本文综述了近年来人脸面部表情识别的研究进展。首先对面部表情识别技术的研究背景和发展历程作了简单的回顾，然后着重介绍了几种主要的识别方法，并力争从理论上对各种方法进行分析和比较，最后简单地讨论了进一步提高面部表情识别率的难点和必须考虑的几个重要方面，进而展望了人脸面部表情识别技术的发展方向。

提出了一种利用全局和时序结构的组合特征以及MMD进行情感特征识别的方法。对于从10名话者中搜集的带有欢快，愤怒，惊奇和悲伤4种情感的1000句语句，利用提出的新的识别方法获得了94%的平均情感识别率。

A new nonlinear feature extraction method called kernel Foley-Sammon optimal discriminant vectors (KFSODV) is presented in this paper. This new method extends the well-known Foley-Sammon optimal discriminant vectors (FSODV) method from linear domain to a nonlinear domain via the kernel trick that has been used in support vector machine (SVM) and other commonly used kernel based methods. The proposed method also provides an effective technique to solve the so-called "small sample size" (SSS) problem which exists in many classification problems such as face recognition. We give the derivation of KFSODV and the comparison to other commonly used kernel based methods. The experimental results on both simulated and real data confirm that the KFSODV method is superior to other commonly used kernel based methods in terms of the performance of discrimination.