NDLI: Pixel-Based Hierarchical-Feature face detection

Content Provider	IEEE Xplore Digital Library
Author	Jing-Ming Guo Min-Feng Wu
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan (Jing-Ming Guo; Min-Feng Wu)
Abstract	In this paper, the Pixel-Based Hierarchical-Feature Adaboosting (PBHFA) method is presented. The purpose of this approach is the reduction of computation complexity in face-detection tasks. The Adaboosting method has attracted attention for its efficient face-detection performance. However, in the training process, the large number of possible Haar-like features in a standard sub-window becomes time consuming, which makes specific environment feature adaptation extremely difficult. For this object, the PBHFA is proposed as a possible solution. Given a M × N sub-window, the number of possible PBH features is simplified down to a level less than M × N, which significantly reduces the length of the training period by a factor of 1500. Moreover, when the trained PBH features are employed for practical face-detection tasks, the hierarchically structural pattern matching also has lower complexity than that of the integral-image based approach in the traditional Adaboosting method. As documented in experimental results, with the MIT-CMU profile test set are examined, the proposed PBH features have shown significantly more effective than Haar-like features.
Starting Page	1638
Ending Page	1641
File Size	1864739
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495533
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face detection Support vector machines Support vector machine classification Radio frequency Detectors Computational efficiency Neural networks Pattern matching Testing Computer vision hierarchical feature Adaboost face detection
Content Type	Text
Resource Type	Article

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