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- Title
Cross-view face recognition via structured dictionary based domain shift.
- Authors
Xue Chen; Chunheng Wang; Baihua Xiao; Xinyuan Cai
- Abstract
View variation is a major challenge in face recognition. In this study, the authors propose a novel cross-view face recognition method by seeking potential intermediate domains between the source and target views to model the connection of varying-views faces. Specifically, each intermediate domain is associated with a dictionary subspace. Learning proceeds in two phases. First, the authors discriminatively train a sub-dictionary for each subclass of data, which then compose a structured dictionary of powerful reconstructive and discriminative capability on the source data. Secondly, the authors gradually adapt the source domain dictionary to the target domain by incrementally reducing the reconstruction error on the target data, which forms a smooth transition path connecting the source and target domains. Instead of updating the structured dictionary integrally, the authors develop a refined sub-dictionarybased updating algorithm, which makes the intermediate dictionaries fit on the target data better and faster. Finally, the authors apply invariant sparse codes across the source, intermediate and target domains to render domain-shared representations, where the sample differences caused by view changes are reduced. Experiments on the CMU-PIE and Multi-PIE dataset demonstrate the effectiveness of the proposed method.
- Subjects
FACE perception; SUBSPACES (Mathematics); DATA analysis; INVARIANTS (Mathematics); COMPUTER algorithms
- Publication
IET Computer Vision (Wiley-Blackwell), 2015, Vol 9, Issue 4, p531
- ISSN
1751-9632
- Publication type
Article
- DOI
10.1049/iet-cvi.2014.0092