Cuong Nguyen Khac and Ju H. Park and Ho-Youl Jung
Face Detection using Variance based HaarLike feature and SVM
2947 - 2950
2009
3
12
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/5120
https://publications.waset.org/vol/36
World Academy of Science, Engineering and Technology
This paper proposes a new approach to perform the
problem of realtime face detection. The proposed method combines
primitive HaarLike feature and variance value to construct a new
feature, socalled Variance based HaarLike 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 nonface 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 HaarLike feature and
SVM can be much more efficient than face detection system using
primitive HaarLike feature and AdaBoost. We tested our method on
two Face databases and one NonFace database. We have obtained
96.17 of correct detection rate on YaleB face database, which is
higher 4.21 than that of using primitive HaarLike feature and
AdaBoost.
Open Science Index 36, 2009