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DOI:
Journal of Software:2004.15(11):1607-1615

纹理约束下的人脸特征点跟踪
宋刚,艾海舟,徐光祐
(清华大学,智能技术与系统国家重点实验室,北京,100084;清华大学,计算机科学与技术系,北京,100084)
Texture Constrained Facial Feature Point Tracking
SONG Gang,AI Hai-Zhou,XU Guang-You
()
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Received:August 11, 2003    Revised:June 10, 2004
> 中文摘要: 将Lucas-Kanade光流跟踪算法与人脸特征点定位的统计模型DAM(direct appearance model)在Bayesian框架下结合起来,提出了视频中人脸特征点定位与跟踪的一种混合模型方法.利用Lucas-Kanade算法预测人脸特征点的位置,充分利用了帧间的相关信息,提高了跟踪的速度.通过DAM中纹理对形状的约束,在提高跟踪精度的同时增强了整个算法的鲁棒性.实验表明,这种方法可以很好地适应人脸的多种运动,可用于人脸识别或3D人脸建模.
Abstract:In this paper, a facial feature point tracking scheme is proposed by integrating Lucas-Kanade optical flow tracking algorithm and the face alignment statistical model, DAM (direct appearance model), together in a Bayesian framework. The prediction of feature positions from Lucas-Kanade algorithm exploits the inter-frame correlations and accelerates the tracking speed. The texture-shape constraint under DAM improves the localization accuracy and robustness. Experiments show that this method adapts well to the various face movements. It can be used in face recognition or 3D face modeling.
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基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60332010, 60273005 (国家自然科学基金) Supported by the National Natural Science Foundation of China under Grant Nos.60332010, 60273005 (国家自然科学基金)
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宋刚,艾海舟,徐光祐.纹理约束下的人脸特征点跟踪.软件学报,2004,15(11):1607-1615

SONG Gang,AI Hai-Zhou,XU Guang-You.Texture Constrained Facial Feature Point Tracking.Journal of Software,2004,15(11):1607-1615