Face recognition using machine vision for Security system

Abstract

The aim of this project is to design and develop a face recognition based security system using machine vision. In this paper, a face recognition system using Principal Component Analysis (PCA) with Euclidean distance classifier is proposed. PCA is selected as it is less sensitive to noise and interference, besides reducing the number of variables in face recognition which reduce computational time and increase accuracy. A number of experiments were done to evaluate the performance of the face recognition system. A training database of 20 students is created and used in this project. Overall, the face recognition system on static face images has 86% success rate and 14% error rate which is mainly due to the variation of pose orientation. It is also found that the threshold value is an important factor for the performance of face recognition. The distance of the person to the camera play a role as the further the person is away from the camera, the more blur the face image captured. As for the anti-spoofing approach, the proposed detection of eye blinking is successfully tested with training eyes open and eyes close images.