Automatic facial expression recognition: feature extraction and selection |
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Authors: | Seyed Mehdi Lajevardi Zahir M Hussain |
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Affiliation: | (1) Machine Vision Group, Department of Computer Science and Engineering, University of Oulu, PO Box 4500, 90014 Oulu, Finland;(2) Nokia Research Center, Palo Alto, CA, USA; |
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Abstract: | In this paper, we investigate feature extraction and feature selection methods as well as classification methods for automatic
facial expression recognition (FER) system. The FER system is fully automatic and consists of the following modules: face
detection, facial detection, feature extraction, selection of optimal features, and classification. Face detection is based
on AdaBoost algorithm and is followed by the extraction of frame with the maximum intensity of emotion using the inter-frame
mutual information criterion. The selected frames are then processed to generate characteristic features using different methods
including: Gabor filters, log Gabor filter, local binary pattern (LBP) operator, higher-order local autocorrelation (HLAC)
and a recent proposed method called HLAC-like features (HLACLF). The most informative features are selected based on both
wrapper and filter feature selection methods. Experiments on several facial expression databases show comparisons of different
methods. |
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Keywords: | |
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