Deep Heterogeneous Face Recognition Networks Based on Cross-Modal Distillation and an Equitable Distance Metric
Christopher Reale, Hyungtae Lee, Heesung Kwon · 2017
In this work we present three methods to improve a deep convolutional neural network approach to near-infrared heterogeneous face recognition. We first present a method to distill extra information from a pre-trained visible face network through the output logits of the network. Next, we put forth an altered contrastive loss function that uses the ℓ1norm instead of the ℓ2norm as a distance metric. Finally, we propose to improve the initialization network by training it for more iterations. We present the results of experiments of these methods on two widely used near-infrared heterogeneous face recognition datasets and compare them to the state-of-the-art.