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Proceedings Paper

Face detection by learning from positive examples
Author(s): Shijin Li; Jianfeng Lu; Jingyu Yang
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Paper Abstract

Many existing methods for face detection using both the positive examples (faces) and negative examples (nonfaces). By learning only from the positive examples, a novel face detection algorithm is invented, which is made up of two parts of research works. The first one is a frontal-view upright face detection algorithm based on the well-known singular value feature (SVF) and Hidden Markov models (HMM). The algorithm couples the virtues of both the SVF and HMM and produces excellent detection results. Firstly, it is tested on the second part of a large face image library NUSTFDB603-II whose first part is used to train the HMM and where there are 954 face images of 96 persons. The detection rate is 98.32% while only one false alarm is reported. Then it is tested on a collect photo album and has detected the 85.1 percent of its 484 people, while 97 false alarms are also reported. The second part of our algorithm is the extension of the first one to rotation-invariant face detection. Several HMMs are employed simultaneously and the angle of the "face" image is obtained. Then the HMM for detecting the upright faces is employed to verify the faceness of the test pattern. This rotation-invariant algorithm is tested on another image set where there are 173 persons whose faces are rotated randomly. The detection rate is 72.2%, and 34 false alarms are reported.

Paper Details

Date Published: 11 October 2000
PDF: 12 pages
Proc. SPIE 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, (11 October 2000); doi: 10.1117/12.403777
Show Author Affiliations
Shijin Li, Hohai Univ. (China)
Jianfeng Lu, Nanjing Univ. of Science and Technology (China)
Jingyu Yang, Nanjing Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 4197:
Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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