Entropy based Binary Particle Swarm Optimization and classification for ear detection

Ear detection in facial images under uncontrolled environments with varying occlusion, pose, background and lighting conditions is challenging. In this paper, we propose a novel technique, namely Entropic Binary Particle Swarm Optimization (EBPSO) which generates an entropy map, the highest value of which is used to localize the ear in a face image. Also, Dual Tree Complex Wavelet Transform (DTCWT) based background pruning is used to eliminate most of the background in the face image. This is achieved as a result of DTCWT highlighting the strong curves in the foreground. The resulting preprocessed image contains the salient facial features and prepares the ground for ear detection. The Entropy based classifier successfully demarcates the ear regions from other facial features, based on observed patterns of entropy. Experimental results show the promising performance of EBPSO for ear detection on four benchmark face databases: CMU PIE, Pointing Head Pose, Color FERET and UMIST.