Abstract

Recently, face recognition has attracted significant attention from the researchers and scientists in various fields of research, such as biomedical informatics, pattern recognition, vision, etc due its applications in commercially available systems, defense and security purpose. In this paper a practical method for face reorganization utilizing head cross section data based on Procrustes analysis is proposed. This proposed method relies on shape signatures of the contours extracted from face data. The shape signatures are created by calculating the centroid distance of the boundary points, which is a translation and rotation invariant signature. The shape signatures for a selected region of interest (ROI) are used as feature vectors and authentication is done using them. After extracting feature vectors a comparison analysis is performed utilizing Procrustes distance to differentiate their face pattern from each other. The proposed scheme attains an equal error rate (EER) of 4.563% for the 400 head data for 100 subjects. The performance analysis of face recognition was analyzed based on K nearest neighbour classifier. The experimental results presented here verify that the proposed method is considerable effective.

Key words: Face, biometrics, Procrustes distance, equal error rate, k nearest classifier.