Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms. (15th April 2010)

• Record Type: Journal Article
• Authors: Lee Lee, Chien-Cheng Chien-Cheng; Huang Huang, Shin-Sheng Shin-Sheng; Shih Shih, Cheng-Yuan Cheng-Yuan
• Other Names: Hwang Hwang Jenq-Neng Jenq-Neng Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-04-15
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/596842
• Languages: English
• ISSNs: 1687-6172

Abstract

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.