Abstract
Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap).
Highlights
Facial expressions are the facial changes indicating a person’s internal affective states, intentions or social communications
The performance of KDIsomap is compared with principal component analysis (PCA) [4], LDA [5], kernel principal component analysis (KPCA) [24], kernel linear discriminant analysis (KLDA) [29], and kernel isometric mapping (KIsomap)
Note that the embedded dimensions of LDA and KLDA are limited to the range [2,6] because they can only find at most c − 1 meaningful embedded features, where c is the number of facial expression classes
Summary
Facial expressions are the facial changes indicating a person’s internal affective states, intentions or social communications. Based on the shown facial expressions, human face is the predominant mode of expressing and interpreting affective states of human beings. Automatic facial expression recognition impacts important applications in many areas such as natural human computer interactions, image retrieval, talking heads and human emotion analysis [1]. Automatic facial expression recognition has been increasingly attracting attention and has become an important issue in the scientific community, since facial expressions are one of the most powerful, nature and immediate means for human beings to communicate their emotions and intentions. A basic automatic facial expression recognition system generally consists of three steps [2]: face acquisition, facial feature extraction and representation, and facial expression classification. Face acquisition is a preprocessing stage to detect or locate the face region in input images or sequences
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