A Face Tracking and Recognition Using AdaBoost and PCA Algorithm

Abstract

In this paper, we present a method of real-time face recognition for embedded system of wireless mirror display system at smart home. To this end, the proposed approach takes advantages of the improved integral image concept and a human facial detecting training method of Adaptive Boosting (Adaboost). The facial recognition approach combines the features of whole-face and facial areas (eyes, nose and mouth) by using Principal Component Analysis (PCA) method. All the compared features are weighted to determine who the test image is. The paper also presents an analysis and evaluation regarding the influence of the variation of facial illumination, pose and expressions of the input images. To decrease the influence of illumination variation, Retinex algorithm is adopted. To adjust the incline pose, the eye-line is detected to adjust face to horizontal level. To reduce the influence of expression, the weights of those features are decreased according to the affect by facial expression. The experimental results, showing the availability and the usability of the proposed approach with recognition rate up to 96%, are included.