Multi-observation face recognition in videos based on label propagation
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2015)
摘要
In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons' identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video sequences extracted from the popular TV-series `Friends' show a distinct advantage of the proposed method over the existing standard graph construction methods.
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关键词
face recognition,single object recognition,multi-observation object recognition,person identity assessment,social media search engines,Facebook,Youtube,label propagation,KNN graph,video sequences,adaptive graph construction,two-phase scheme,minimization function,sparse affinity matrix weights,Honda video database,HVDB,standard graph construction methods
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