Salient feature and reliable classifier selection for facial expression classification

• Published in: Pattern recognition Vol. 43; no. 3; pp. 972 - 986
• Main Authors: Kyperountas, Marios, Tefas, Anastasios, Pitas, Ioannis
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2010; Elsevier
• Subjects: Applied sciences; Classifier selection; Exact sciences and technology; Facial expression classification; Information, signal and communications theory; Miscellaneous; Salient feature selection; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Two-class classification; Facial expression classification; Classifier selection; Salient feature selection; Two-class classification; Performance evaluation; Separability; Discriminant analysis; Automatic classification; Iterative method; Signal classification; Optical fiber network; Synchronous digital hierarchy; Database; Forward error correction; Signal processing; Feature extraction; Optical fiber communication; Facial expression
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2009.07.007

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.