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Learning Invariant Deep Representation for NIR-VIS Face Recognition
Ran He1,2,3,4; Xiang Wu1,2; Zhenan Sun1,2,3,4; Tieniu Tan1,2,3,4
Conference NameAmerican Association for AI National Conference(AAAI)
Conference Date4 – 9 February, 2017
Conference PlaceSan Francisco, California USA
Visual versus near infrared (VIS-NIR) face recognition is still a challenging heterogeneous task due to large appearance difference between VIS and NIR modalities. This paper presents a deep convolutional network approach that uses only one network to map both NIR and VIS images to a compact Euclidean space. The low-level layers of this network are trained only on large-scale VIS data. Each convolutional layer is implemented by the simplest case of maxout operator. The highlevel layer is divided into two orthogonal subspaces that contain modality-invariant identity information and modalityvariant spectrum information respectively. Our joint formulation leads to an alternating minimization approach for deep representation at the training time and an efficient computation for heterogeneous data at the testing time. Experimental evaluations show that our method achieves 94% verification rate at FAR=0.1% on the challenging CASIA NIR-VIS 2.0 face recognition dataset. Compared with state-of-the-art methods, it reduces the error rate by 58% only with a compact 64-D representation.
Document Type会议论文
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
4.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ran He,Xiang Wu,Zhenan Sun,et al. Learning Invariant Deep Representation for NIR-VIS Face Recognition[C],2017.
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