IMAGE PROCESSING AND FACE DETECTION SYSTEM WITH FACE RECOGNITION BASED FACE ALGORITHM EIGENVECTORS PRINCIPAL COMPONENT ANALYSIS (PCA)

Abstract

The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisherface method. Facial image recognition Eigenface method is based on the reduction of face-dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using eigenfaces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image.