TY - JFULL AU - Cuong Nguyen Khac and Ju H. Park and Ho-Youl Jung PY - 2009/1/ TI - Face Detection using Variance based Haar-Like feature and SVM T2 - International Journal of Computer and Information Engineering SP - 2946 EP - 2950 VL - 3 SN - 1307-6892 UR - https://publications.waset.org/pdf/5120 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 36, 2009 N2 - This paper proposes a new approach to perform the problem of real-time face detection. The proposed method combines primitive Haar-Like feature and variance value to construct a new feature, so-called Variance based Haar-Like feature. Face in image can be represented with a small quantity of features using this new feature. We used SVM instead of AdaBoost for training and classification. We made a database containing 5,000 face samples and 10,000 non-face samples extracted from real images for learning purposed. The 5,000 face samples contain many images which have many differences of light conditions. And experiments showed that face detection system using Variance based Haar-Like feature and SVM can be much more efficient than face detection system using primitive Haar-Like feature and AdaBoost. We tested our method on two Face databases and one Non-Face database. We have obtained 96.17% of correct detection rate on YaleB face database, which is higher 4.21% than that of using primitive Haar-Like feature and AdaBoost. ER -