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Coupled Deep Learning for Heterogeneous Face Recognition
Xiang Wu1,2; Lingxiao Song1,2; Ran He1,2; Tieniu Tan1,2
2018
会议名称American Association for AI National Conference(AAAI)
会议日期February 2–7, 2018
会议地点New Orleans, Louisiana, USA
摘要
Heterogeneous face matching is a challenge issue in face recognition due to large domain difference as well as insufficient pairwise images in different modalities during training. This paper proposes a coupled deep learning (CDL) approach for the heterogeneous face matching. CDL seeks a shared feature space in which the heterogeneous face matching problem can be approximately treated as a homogeneous facematchingproblem.TheobjectivefunctionofCDLmainly includes two parts. The first part contains a trace norm and a block-diagonal prior as relevance constraints, which not only make unpaired images from multiple modalities be clustered and correlated, but also regularize the parameters to alleviate overfitting. An approximate variational formulation is introduced to deal with the difficulties of optimizing low-rank constraint directly. The second part contains a cross modal ranking among triplet domain specific images to maximize the margin for different identities and increase data for a small amount of training samples. Besides, an alternating minimization method is employed to iteratively update the parameters of CDL. Experimental results show that CDL achieves better performance on the challenging CASIA NIR-VIS 2.0 face recognition database, the IIIT-D Sketch database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF), which significantly outperforms state-of-the-art heterogeneous face recognition methods.
 
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19722
专题智能感知与计算研究中心
通讯作者Ran He
作者单位1.Center for Research on Intelligent Perception and Computing, CASIA
2.National Laboratory of Pattern Recognition, CASIA
推荐引用方式
GB/T 7714
Xiang Wu,Lingxiao Song,Ran He,et al. Coupled Deep Learning for Heterogeneous Face Recognition[C],2018.
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