Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101109
Title: Person independent facial expression analysis using Gabor features and genetic algorithm
Authors: Shojaeilangari, Seyedehsamaneh
Yau, Wei-Yun
Teoh, Eam Khwang
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2011
Source: Shojaeilangari, S., Yau, W. Y., & Teoh, E. K. (2011). Person independent facial expression analysis using Gabor features and genetic algorithm. 8th International Conference on Information, Communications & Signal Processing, 1-5.
Abstract: Over the last decade, automated analysis of human affective behavior has become an active research area in computer science, psychology, neuroscience, and related fields. This study investigates the application of Gabor filter based features in combination of Genetic Algorithm (GA) and Support Vector Machine (SVM) for dynamic analysis of six basic facial expressions from video sequences. Traditionally, a set of Gabor filters is used for feature extraction from static images of face. However, we employed Sum of Difference (SOD) approach to analysis the dynamics of facial expression from a video sequence. We also used GA to overcome the problem of high dimensional feature vectors and computation cost. A local Gabor filter bank with selected frequencies and orientations is produced by GA. The experimental results show that the proposed method is effective for temporal analysis of affective states. The detection rate of six basic emotions has been reached to 92.97% for Cohn-Kanade (CK+) database.
URI: https://hdl.handle.net/10356/101109
http://hdl.handle.net/10220/18014
DOI: 10.1109/ICICS.2011.6173537
Rights: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICICS.2011.6173537].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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