Salient feature and reliable classifier selection for facial expression classification

  • Authors:

  • Marios Kyperountas;Anastasios Tefas;Ioannis Pitas

  • Affiliations:

  • Aristotle University of Thessaloniki, Department of Informatics, Artificial Intelligence and Information Analysis Laboratory, Box 451, 54006 Thessaloniki, Greece;Aristotle University of Thessaloniki, Department of Informatics, Artificial Intelligence and Information Analysis Laboratory, Box 451, 54006 Thessaloniki, Greece;Aristotle University of Thessaloniki, Department of Informatics, Artificial Intelligence and Information Analysis Laboratory, Box 451, 54006 Thessaloniki, Greece and Informatics and Telematics Ins ...

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

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.