Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/67412
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Type: Conference paper
Title: Face detection from few training examples
Author: Shen, C.
Paisitkriangkrai, S.
Zhang, J.
Citation: Proceedings of 15th IEEE International Conference on Image Processing (ICiP'08), 12-15 October, 2008; pp. 2764-2767
Publisher: IEEE
Publisher Place: Online
Issue Date: 2008
Series/Report no.: IEEE International Conference on Image Processing ICIP
ISBN: 1424417643
9781424417643
ISSN: 1522-4880
Conference Name: IEEE International Conference on Image Processing (15th : 2008 : California)
Statement of
Responsibility: 
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 [1] 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
RMID: 0020112863
DOI: 10.1109/ICIP.2008.4712367
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4667700
Appears in Collections:Computer Science publications

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