Enhanced face recognition using 8-Connectivity-of-Skin-Region and Standard-Deviation-based-Pose-Detection as preprocessing techniques

Abstract

Face appearance drastically changes under varying background, pose and illumination conditions. Face Recognition (FR) in such varying conditions becomes a difficult and challenging task. In this paper, we propose three novel techniques, viz., Face Detection based on 8-Connectivity-of-Skin-Region (FDCSR), Standard Deviation based Pose Detection (SDPD) and Gamma Ray Burst Rhombus Star (GRBRS) feature mask to improve the performance of FR systems. FDCSR is used as a preprocessing step to remove cluttered background from the image. SDPD is also a preprocessing step where pose neutralization technique is employed. GRBRS feature mask on the Fast Fourier Transform (FFT) of the preprocessed image is used to extract the salient features of the face. Binary Particle Swarm Optimization (BPSO) feature selection algorithm is used to search the feature vector space for the optimal feature subset. Experimental results show promising performance of the proposed techniques for FR on four benchmark face databases, namely, Color FERET, CMUPIE, HP and FEI.

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

“…In their work the algorithm was adapted with a deterministic parameter control technique which decreases C1 and increases C2 exponentially with time. Like others, Vora et al (2014) tried the FS on Gamma Ray Burst Rhombus Star (GRBRS) feature mask space and Varun et al (2015) on Block-wise Hough Transform (HT) feature space. The nal work which used the distance between class as a tness evaluation was proposed by Varadarajan et al (2015).…”

“…In this branch, we can cite the Particle Swarm Optimization (PSO) algorithm (Eberhart and Shi;, one of the wellknown algorithms among researchers, and it is inspired by the coordinated movement of sh schools and bird ocks. Among many versions of PSO, its binary version has been widely used to nd the most discriminative set of features in facial images improving FR systems (Vora et al;Varun et al;Varadarajan et al;. Another popular SIbased algorithm is the Ant Colony Optimization (ACO) (Dorigo and Stutzle;2003), which is inspired by the collective behavior of ants in nding the shortest path between the nest and the food source through a substance called pheromone.…”