Graphical Representation for Heterogeneous Face Recognition
- 16 March 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 39 (2), 301-312
- https://doi.org/10.1109/tpami.2016.2542816
Abstract
Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification. HFR plays an important role in both biometrics research and industry. In spite of promising progresses achieved in recent years, HFR is still a challenging problem due to the difficulty to represent two heterogeneous images in a homogeneous manner. Existing HFR methods either represent an image ignoring the spatial information, or rely on a transformation procedure which complicates the recognition task. Considering these problems, we propose a novel graphical representation based HFR method (G-HFR) in this paper. Markov networks are employed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration. A coupled representation similarity metric (CRSM) is designed to measure the similarity between obtained graphical representations. Extensive experiments conducted on multiple HFR scenarios (viewed sketch, forensic sketch, near infrared image, and thermal infrared image) show that the proposed method outperforms state-of-the-art methods.Keywords
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Funding Information
- National Natural Science Foundation of China (61432014, 61501339, 61571343)
- Fundamental Research Funds for the Central Universities (BDZ021403, XJS15049, JB160104, XJS15068, JB149901)
- Microsoft Research Asia Project based Funding (FY13-RES-OPP-034)
- Changjiang Scholars and Innovative Research Team in University of China (IRT13088)
- Shaanxi Innovative Research
- Key Science and Technology (2012KCT-02)
- China Post-Doctoral Science Foundation (2015M580818)
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