Face Recognition for Biometric Security System

Abstract

Biometrics is used for human recognition which consists of identification and verification. In an identification application, the biometric device reads a sample and compares that sample against every record or template in the database. Identification applications are common when the goal is to identify criminals, terrorists, or other particularly through surveillance. Personal face recognition is crucial for applications such as access control, smart card verification, surveillance, human-computer interaction, etc. Also, faces are integral to human interaction. Manual facial recognition is already used in everyday authentication applications. In this paper, a novel subspace method is proposed for face recognition. A new face recognition method DiaPCA is based on PCA (principal Component Analysis) and KNN (Kth nearest neighbor classifier). The recognition process consists of three stages: preprocessing, dimension reduction by using PCA, and matching of the extracted feature using KNN. Combination of DiaPCA and KNN is used for improving the capability of PCA when a few samples of images are available. In contrast to standard PCA, DiaPCA directly seeks the optimal projective vectors from diagonal face images without image-to-vector transformation. DiaPCA reserves the correlations between variations of rows and those of columns of images. DiaPCA is much more accurate than PCA. The motivation of this research is to provide the personal identification from the National Registration Card (NRC card).