Ensemble Classification Based on ICA for Face Recognition

This paper proposes a new face recognition approach by using Independent Component Analysis (ICA) and Ensemble Classifiers based on Support Vector Machine (SVM). Firstly, to improve the quality of the face images, a series of image pre-processing techniques are used. Then the ICA based on Kernel Principal Component Analysis (KPCA) and FastICA is employed to extract features. At last, appropriate classifiers based on SVM are selected to construct the classification committee using Binary Particle Swarm Optimization (BPSO). The experimental results show that the proposed framework is efficient for face recognition.