Face Recognition Based on Independent Component Analysis and Fuzzy Support Vector Machine

This paper presents a new approach to face recognition using independent component analysis (ICA) and fuzzy support vector machine (FSVM). Firstly, 2D wavelet transform is adopted to obtain different level of wavelet coefficients. Secondly ICA is applied on the low-frequency which contains most discrimination information of the original face image. One criterion that not all ICs are useful for face recognition is demonstrated and a rule for selecting ICs is proposed. For reducing the computational cost, a fast ICA method is proposed. Then, FSVM classifier is designed for recognition. Lastly, this algorithm is tested on the ORL and Yale face databases and the experimental result is encouraging, which achieves comparatively high recognition accuracy and is more computationally efficient than using general PCA-based recognition method