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Face Tracking and Recognition via Incremental Local Sparse Representation
Chao Wang; Yunhong Wang; Zhaoxiang Zhang; Yiding Wang
2013-07-26
会议名称International Conference on Image and Graphics
会议录名称ICIG 2013
会议日期26-28 July 2013
会议地点Qingdao, China
摘要This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. We first develop a robust face tracking algorithm based on the local sparse appearance. This sparse representation model exploits both partial and spatial information of the face based on a covariance pooling method. Following in the face recognition stage, with the employment of a novel template update strategy, our recognition algorithm adapts the template to appearance change and reduces the influence of occlusion and illumination variation. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database. Our proposed method produces a high face recognition results on over 93% of all videos. The tracking results on challenging videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. On the challenging data set in which faces are undergo occlusion and illumination variation, our proposed method also consistently demonstrates a high recognition rate.
关键词Video Analysis Face Recognition Face Tracking Video-based Face Recognition
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13289
专题类脑智能研究中心
通讯作者Zhaoxiang Zhang
推荐引用方式
GB/T 7714
Chao Wang,Yunhong Wang,Zhaoxiang Zhang,et al. Face Tracking and Recognition via Incremental Local Sparse Representation[C],2013.
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