Salient feature and reliable classifier selection for facial expression classification

详细信息    查看全文

• 作者：Marios Kyperountas ; Anastasios Tefas ; Ioannis Pitas
• 关键词：Facial expression classification ; Salient feature selection ; Classifier selection ; Two-class classification
• 刊名：Pattern Recognition
• 出版年：2010
• 出版时间：March 2010
• 年：2010
• 卷：43
• 期：3
• 页码：972-986
• 全文大小：545 K

文摘

A novel facial expression classification (FEC) method is presented and evaluated. The classification process is decomposed into multiple two-class classification problems, a choice that is analytically justified, and unique sets of features are extracted for each classification problem. Specifically, for each two-class problem, an iterative feature selection process that utilizes a class separability measure is employed to create salient feature vectors (SFVs), where each SFV is composed of a selected feature subset. Subsequently, two-class discriminant analysis is applied on the SFVs to produce salient discriminant hyper-planes (SDHs), which are used to train the corresponding two-class classifiers. To properly integrate the two-class classification results and produce the FEC decision, a computationally efficient and fast classification scheme is developed. During each step of this scheme, the most reliable classifier is identified and utilized, thus, a more accurate final classification decision is produced. The JAFFE and the MMI databases are used to evaluate the performance of the proposed salient-feature-and-reliable-classifier selection (SFRCS) methodology. Classification rates of 96.71 % and 93.61 % are achieved under the leave-one-sample-out evaluation strategy, and 85.92 % under the leave-one-subject-out evaluation strategy.