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|Title:||Face detection from few training examples|
|Citation:||Proceedings of 15th IEEE International Conference on Image Processing (ICiP'08), 12-15 October, 2008; pp. 2764-2767|
|Series/Report no.:||IEEE International Conference on Image Processing ICIP|
|Conference Name:||IEEE International Conference on Image Processing (15th : 2008 : California)|
|Chunhua Shen, Sakrapee Paisitkriangkrai and Jian Zhang|
|Abstract:||Face detection in images is very important for many multimedia applications. Haar-like wavelet features have become dominant in face detection because of their tremendous success since Viola and Jones  proposed their AdaBoost based detection system. While Haar features' simplicity makes rapid computation possible, its discriminative power is limited. As a consequence, a large training dataset is required to train a classifier. This may hamper its application in scenarios that a large labeled dataset is difficult to obtain. In this work, we address the problem of learning to detect faces from a small set of training examples. In particular, we propose to use co- variance features. Also for better classification performance, linear hyperplane classifier based on Fisher discriminant analysis (FDA) is proffered. Compared with the decision stump, FDA is more discriminative and therefore fewer weak learners are needed. We show that the detection rate can be significantly improved with covariance features on a small dataset (a few hundred positive examples), compared to Haar features used in current most face detection systems.|
|Rights:||© Copyright 2011 IEEE – All Rights Reserved|
|Appears in Collections:||Aurora harvest|
Computer Science publications
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