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Content Provider | IEEE Xplore Digital Library |
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Author | Jie Chen Xilin Chen Wen Gao |
Copyright Year | 2004 |
Description | Author affiliation: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., China (Jie Chen; Xilin Chen; Wen Gao) |
Abstract | Over the past ten years, face detection has been thoroughly studied in computer vision research for its interesting applications. However, all of the state-of-the-art statistical methods suffer from the data collection for training a classifier. This paper presents a self-adaptive genetic algorithm (GA)-based method to swell face database through re-sampling from the existing faces. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. To verify the generalization capability of the proposed method, we also use the expanded database to train an Adaboost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods. |
Starting Page | 822 |
Ending Page | 825 |
File Size | 357727 |
Page Count | 4 |
File Format | |
ISBN | 0769521282 |
ISSN | 10514651 |
DOI | 10.1109/ICPR.2004.1334655 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2004-08-26 |
Publisher Place | UK |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Face detection Genetic algorithms Testing Snow Image databases Detectors Learning systems Computer science Computer vision Application software |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition |
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