Adaptive BPSO based feature selection and skin detection based background removal for enhanced face recognition

Face recognition under varying background and pose is challenging, and extracting background and pose invariant features is an effective approach to solve this problem. This paper proposes a skin detection-based approach for enhancing the performance of a Face Recognition (FR) system, employing a unique combination of Skin based background removal, Discrete Wavelet Transform (DWT), Adaptive Multi-Level Threshold Binary Particle Swarm Optimization (ABPSO) and an Error Control Feedback (ECF) loop. Skin based background removal is used for efficient background removal and ABPSO-based feature selection algorithm is used to search the feature space for the optimal feature subset. The ECF loop is used to neutralize pose variations. Experimental results, obtained by applying the proposed algorithm on Color FERET and CMUPIE face databases, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and substantial reduction in the number of features are observed.