Adversarial Discriminative Heterogeneous Face Recognition

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	Adversarial Discriminative Heterogeneous Face Recognition
	Lingxiao Song 1,2; Man Zhang 1,2; Xiang Wu1,2 ; Ran He1,2,3
	2018
会议日期	February 2–7, 2018
会议地点	New Orleans, Louisiana, USA
英文摘要	The 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.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/19717]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Ran He
作者单位	1.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
推荐引用方式 GB/T 7714	Lingxiao Song,Man Zhang,Xiang Wu,et al. Adversarial Discriminative Heterogeneous Face Recognition[C]. 见:. New Orleans, Louisiana, USA. February 2–7, 2018.