Abstract—With the continuous progress of human computer interaction, face detection as well as facial expression recognition is gaining the attention of researchers from the fields of security, psychology, image processing, and computer vision. In this area the most challenging thing is to recognize accurate facial expression with minimum time requirement. In this work, our main focus is to minimize the time using fusion based Independent Component Analysis (ICA). Research studies show ICA has significant success on face image analysis. Among several architectures of ICA we mainly used here Gaussian kernel based FastICA algorithm due to time efficiency. We apply FastICA on whole faces to recognize facial expressions. Also we apply FastICA on different facial parts, by proposing two algorithms namely WAPA-FastICA and OEPA-FastICA, to analyze the influence of different parts for several basic emotions. Our experiment shows OEPA-FastICA and WAPA-FastICA outperforms existing predominant FastICA algorithm. We also compared these proposed algorithms with our previous PCA based facial expression recognition work.
Index Terms—OEPA: optimal expression specific parts accumulation, WAPA: weighted all parts accumulation algorithm, ICA: independent component analysis, FER: facial expression recognition, LS-ICA: locally salient ICA
Cite: Humayra B. Ali and David M W Powers, "Fusion Based FastICA Method: Facial Expression Recognition," Journal of Image and Graphics, Vol. 2, No. 1, pp. 1-7, June 2014. doi: 10.12720/joig.2.1.1-7
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