Face Recognition for Biometric Security System

Abstract

Biometrics is used for human recognition whichconsists of identification and verification. In anidentification application, the biometric device readsa sample and compares that sample against everyrecord or template in the database. Identificationapplications are common when the goal is to identifycriminals, terrorists, or other particularly throughsurveillance. Personal face recognition is crucial forapplications such as access control, smart cardverification, surveillance, human-computerinteraction, etc. Also, faces are integral to humaninteraction. Manual facial recognition is alreadyused in everyday authentication applications.In this paper, a novel subspace method isproposed for face recognition. A new facerecognition method DiaPCA is based on PCA(principal Component Analysis) and KNN (Kthnearest neighbor classifier). The recognition processconsists of three stages: preprocessing, dimensionreduction by using PCA, and matching of theextracted feature using KNN. Combination ofDiaPCA and KNN is used for improving thecapability of PCA when a few samples of images areavailable. In contrast to standard PCA, DiaPCAdirectly seeks the optimal projective vectors fromdiagonal face images without image-to-vectortransformation. DiaPCA reserves the correlationsbetween variations of rows and those of columns ofimages. DiaPCA is much more accurate than PCA.The motivation of this research is to provide thepersonal identification from the NationalRegistration Card (NRC card)