Scenario oriented discriminant analysis for still-to-video face recognition
Xue Chen; Chunheng Wang; Baihua Xiao; Xinyuan Cai
2014
Conference Name2014 IEEE International Conference on Image Processing (ICIP)
Source PublicationInternational Conference on Image Processing (ICIP)
Pages738-742
Conference Date2014
Conference PlacePairs
AbstractIn 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.
KeywordFace Recognition Still-to-video Discriminant Analysis
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/5148
Collection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
Corresponding AuthorChunheng Wang
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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