NDLI: Face Tracking via Block Texture Feature Based Mean Shift

Content Provider	IEEE Xplore Digital Library
Author	Chunshui Zhao Zhiyong Liu Hong Qiao
Copyright Year	2008
Description	Author affiliation: Inst. of Autom., Chinese Acad. of Sci., Beijing (Chunshui Zhao; Zhiyong Liu; Hong Qiao)
Abstract	Face tracking plays an important role in many computer vision applications such as human-robot interaction and visual surveillance. However, it is still a challenging problem, due to various factors related to illumination, cluttered background and poses variations. In this paper, we introduce a novel feature descriptor, namely block binary pattern (BBP), to represent the appearance model of face for the tracking tasks. Compared to local binary pattern (LBP), BBP has the advantage of capturing multi-scale structure, while preserving the robustness to illumination and appearance variations, and meantime, it can be extracted in realtime for real-world applications. Based on the BBP features, we use AdaBoost strategy to select a discriminative features pool. These features can be considered as the prior appearance model of face. We use similarity-based mean-shift, which is the extension of original mean-shift, as the face tracker. Experimental results on challenging sequences validate the effectiveness of our method for face tracking.
Starting Page	190
Ending Page	194
File Size	815749
Page Count	5
File Format	PDF
ISBN	9780769533049
DOI	10.1109/ICNC.2008.790
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Target tracking Automation Surveillance Face recognition Lighting Robustness Large-scale systems Face detection Application software
Content Type	Text
Resource Type	Article

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