Facial affect recognition using regularized discriminant analysis-based algorithms

Authors:
Chien-Cheng Lee;Shin-Sheng Huang;Cheng-Yuan Shih
Affiliations:
Department of Communications Engineering, Yuan Ze University, Chung-Li, Taoyuan County, Taiwan;Department of Communications Engineering, Yuan Ze University, Chung-Li, Taoyuan County, Taiwan;Department of Communications Engineering, Yuan Ze University, Chung-Li, Taoyuan County, Taiwan
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Year:
2010

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Abstract

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.