Disentangled Variational Representation for Heterogeneous Face Recognition

6 Sep 2018Xiang WuHuaibo HuangVishal M. PatelRan HeZhenan Sun

Visible (VIS) to near infrared (NIR) face matching is a challenging problem due to the significant domain discrepancy between the domains and a lack of sufficient data for training cross-modal matching algorithms. Existing approaches attempt to tackle this problem by either synthesizing visible faces from NIR faces, extracting domain-invariant features from these modalities, or projecting heterogeneous data onto a common latent space for cross-modal matching... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Face Verification BUAA-VisNir DVR Wu et al. (2019) TAR @ FAR=0.001 96.9 # 2
TAR @ FAR=0.01 98.5 # 1
Face Verification CASIA NIR-VIS 2.0 DVR Wu et al. (2019) TAR @ FAR=0.001 99.6 # 2
Face Verification Oulu-CASIA NIR-VIS DVR Wu et al. (2019) TAR @ FAR=0.001 84.9 # 2
TAR @ FAR=0.01 97.2 # 2

Methods used in the Paper


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