Abstract
Heterogeneous face recognition (HFR) refers to
matching face images acquired from different domains with wide applications in security scenarios.
This paper presents a deep neural network approach
namely Multi-Margin based Decorrelation Learning (MMDL) to extract decorrelation representations in a hyperspherical space for cross-domain
face images. The proposed framework can be divided into two components: heterogeneous representation network and decorrelation representation learning. First, we employ a large scale of
accessible visual face images to train heterogeneous representation network. The decorrelation
layer projects the output of the first component into
decorrelation latent subspace and obtains decorrelation representation. In addition, we design a
multi-margin loss (MML), which consists of tetrad
margin loss (TML) and heterogeneous angular margin loss (HAML), to constrain the proposed framework. Experimental results on two challenging heterogeneous face databases show that our approach
achieves superior performance on both verification
and recognition tasks, comparing with state-of-theart methods