基于局部SVM分类器的表情识别方法(英文)

Facial expression recognition based on local SVM classifiers

提出了一种新的视频人脸表情识别方法.该方法将识别过程分成人脸表情特征提取和分类2个部分,首先采用基于点跟踪的活动形状模型(ASM)从视频人脸中提取人脸表情几何特征;然后,采用一种新的局部支撑向量机分类器对表情进行分类.在Cohn-Kanade数据库上对KNN、SVM、KNN-SVM和LSVM 4种分类器的比较实验结果验证了所提出方法的有效性.

This paper presents a novel technique developed for the identification of facial expressions in video sources. The method uses two steps： facial expression feature extraction and expression classification. First we used an active shape model （ASM） based on a facial point tracking system to extract the geometric features of facial expressions in videos. Then a new type of local support vector machine （LSVM） was created to classify the facial expressions. Four different classifiers using KNN, SVM, KNN-SVM, and LSVM were compared with the new LSVM. The results on the Cohn-Kanade database showed the effectiveness of our method.