Scenario oriented discriminant analysis for still-to-video face recognition

CASIA OpenIR > 复杂系统管理与控制国家重点实验室 > 影像分析与机器视觉

	Scenario oriented discriminant analysis for still-to-video face recognition
	Xue Chen; Chunheng Wang; Baihua Xiao; Xinyuan Cai
	2014
会议名称	2014 IEEE International Conference on Image Processing (ICIP)
会议录名称	International Conference on Image Processing (ICIP)
页码	738-742
会议日期	2014
会议地点	Pairs
摘要	In the Still-to-Video (S2V) face recognition, each subject is enrolled with only few high resolution images, while the probe is video clips of complex variations. As faces present distinct characteristics under different scenarios, recognition in the original space is obviously inefficient. Therefore, in this paper, we propose a novel discriminant analysis method to learn separate mappings for different scenario patterns (still, video), and further pursue a common discriminant space for the cross-scenario samples. To maximize the intra-individual correlation of samples in the mapping space, we formulate the learning objective by incorporating the intra-class compactness and the inter-class dispersion. The gradient descend algorithm is used to get the optimal solution. Experimental results on the COX-S2V dataset demonstrate the effectiveness of the proposed method and remarkable superiority over state-of-art methods.
关键词	Face Recognition Still-to-video Discriminant Analysis
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/5148
专题	复杂系统管理与控制国家重点实验室_影像分析与机器视觉
通讯作者	Chunheng Wang
作者单位	Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Xue Chen,Chunheng Wang,Baihua Xiao,et al. Scenario oriented discriminant analysis for still-to-video face recognition[C],2014:738-742.

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