Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 40, Issue 5, Pages 1139-1153Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2017.2710183
Keywords
Face recognition; binary feature learning; context-aware; multi-feature learning; heterogeneous face matching
Funding
- National Key Research and Development Program of China [2016YFB 1001001]
- National Natural Science Foundation of China [61672306, 61572271, 61527808, 61373074, 61373090]
- National 1000 Young Talents Plan Program
- National Basic Research Program of China [2014CB349304]
- Ministry of Education of China [20120002110033]
- Tsinghua University Initiative Scientific Research Program
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In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.
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