Deep Heterogeneous Face Recognition Networks Based on Cross-Modal Distillation and an Equitable Distance Metric

Christopher Reale, Hyungtae Lee, Heesung Kwon; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 32-38

Abstract


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 l_1 norm instead of the l_2 norm 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Reale_2017_CVPR_Workshops,
author = {Reale, Christopher and Lee, Hyungtae and Kwon, Heesung},
title = {Deep Heterogeneous Face Recognition Networks Based on Cross-Modal Distillation and an Equitable Distance Metric},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}