Background removal using k-means clustering as a preprocessing technique for DWT based Face Recognition
Ashwitha Surabhi, Shwetha T. Parekh, K. Manikantan, Sakthi Prabha Ramachandran · 2012
Face Recognition (FR) under varying background conditions is challenging, and exacting background invariant features is an effective approach to solve this problem. In this paper, we propose a novel method for background removal based on the k-means clustering algorithm, which lays the ground for DWT-based feature extraction to enhance the performance of a FR system. Individual stages of the FR system are examined and an attempt is made to improve each stage. A Binary Particle Swarm Optimization (BPSO)-based feature selection algorithm is used to search the feature vector space for the optimal feature subset. Experimental results, obtained by applying the proposed algorithm on ORL, UMIST, Extended Yale B and ColorFERET databases, show that the proposed system outperforms other FR systems. A significant increase in the overall recognition rate and a substantial reduction in the number of features are observed.