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 face matching problem. The objective function of CDL mainly
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.

本文提出了一种基于联合深度学习的异构人脸匹配方法，该方法通过在特征空间中寻找共享特征空间，将异构人脸匹配问题近似为同质人脸匹配问题。实验结果表明，该方法可以在具有挑战性的 CASIA NIR-VIS 2.0 人脸识别数据库以及 IIIT-D Sketch 数据库、CUHK Face Sketch（CUFS）和 CUHK Face Sketch FERET（CUFSF）等数据库上实现更好的性能。