Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 16, Issue 4, Pages 874-885Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2008.924344
Keywords
Facial expression recognition; feature selection (FS); model building/modification (MBM); personalization; soft computing technique
Funding
- Ministry of Science and Technology/Korea Science and Engineering Foundation (MOST/KOSEF) [R11-1999-008]
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We propose a design method of personalized classifier with soft computing techniques for automatic facial expression recognition. Motivated by the fact that even though human facial expressions of emotion are often ambiguous and inconsistent, humans are, in general, very good at classifying such complex images. In consideration of individual characteristics, we adopt a similar strategy of building a personalized classifier to enhance the recognition performance. For realization, we use a soft computing technique of neurofuzzy approach. Specifically, two core steps-model building/modification and feature selection-are applied to build a personalized classification structure. The proposed scheme of classifier construction achieves a higher classification rate, minimal network parameters, easy-to-extend structure, and faster computation time, among others. Four sets of facial expression data are chosen and image features are extracted from each of them to show effectiveness of the proposed method, which confirms considerable enhancement of the whole performance.
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