文章基本信息

标题：An Integrated Face Tracking and Facial Expression Recognition System
本地全文：下载
作者：Angappan Geetha ; Venkatachalam Ramalingam ; Sengottaiyan Palanivel 等
期刊名称：Journal of Intelligent Learning Systems and Applications
印刷版ISSN：2150-8402
电子版ISSN：2150-8410
出版年度：2011
卷号：3
期号：4
页码：201-208
DOI：10.4236/jilsa.2011.34023
出版社：Scientific Research Publishing
摘要：This article proposes a feature extraction method for an integrated face tracking and facial expression recognition in real time video. The method proposed by Viola and Jones [1] is used to detect the face region in the first frame of the video. A rectangular bounding box is fitted over for the face region and the detected face is tracked in the successive frames using the cascaded Support vector machine (SVM) and cascaded Radial basis function neural network (RBFNN). The haar-like features are extracted from the detected face region and they are used to create a cascaded SVM and RBFNN classifiers. Each stage of the SVM classifier and RBFNN classifier rejects the non-face regions and pass the face regions to the next stage in the cascade thereby efficiently tracking the face. The performance of tracking is evaluated using one hour video data. The performance of the cascaded SVM is compared with the cascaded RBFNN. The experiment results show that the proposed cascaded SVM classifier method gives better performance over the RBFNN and also the methods described in the literature using single SVM classifier [2]. While the face is being tracked, features are extracted from the mouth region for expression recognition. The features are modelled using a multi-class SVM. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 96.0%.
关键词：Face Detection; Face Tracking; Feature Extraction; Facial Expression Recognition; Cascaded Support Vector Machine; Cascaded Radial Basis Function Neural Network