CASIA OpenIR  > 智能感知与计算研究中心
Adversarial Discriminative Heterogeneous Face Recognition
Lingxiao Song1,2; Man Zhang1,2; Xiang Wu1,2; Ran He1,2,3
Conference NameAmerican Association for AI National Conference(AAAI)
Conference DateFebruary 2–7, 2018
Conference PlaceNew Orleans, Louisiana, USA
AbstractThe gap between sensing patterns of different face modalities remains a challenging problem in heterogeneous face recognition (HFR). This paper proposes an adversarial discriminative feature learning framework to close the sensing gap via adversarial learning on both raw-pixel space and compact feature space. This framework integrates cross-spectral face hallucination and discriminative feature learning into an end-to-end adversarial network. In the pixel space, we make use of generative adversarial networks to perform cross-spectral face hallucination. An elaborate two-path model is introduced to alleviate the lack of paired images, which gives consideration to both global structures and local textures. In the feature space, an adversarial loss and a high-order variance discrepancy loss are employed to measure the global and local discrepancy between two heterogeneous distributions respectively. These two losses enhance domain-invariant feature learning and modality independent noise removing. Experimental results on three NIR-VIS databases show that our proposed approach outperforms state-of-the-art HFR methods, without requiring of complex network or large-scale training dataset.
Document Type会议论文
Corresponding AuthorRan He
Affiliation1.National Laboratory of Pattern Recognition, CASIA
2.Center for Research on Intelligent Perception and Computing, CASIA
3.Center for Excellence in Brain Science and Intelligence Technology, CAS
Recommended Citation
GB/T 7714
Lingxiao Song,Man Zhang,Xiang Wu,et al. Adversarial Discriminative Heterogeneous Face Recognition[C],2018.
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