In this paper, we present a deep coupled learning frame- work to address the problem of matching polarimetric ther- mal face photos against a gallery of visible faces. Polariza- tion state information of thermal faces provides the miss- ing textural and geometrics details in the thermal face im- agery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The pro- posed architecture is able to make full use of the polari- metric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recogni- tion methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embed- ding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superior- ity of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms.

本文介绍了一种深度耦合学习框架，用于解决将极化热量面部照片与可见面部照片库进行匹配的问题，提出了一种耦合的深度神经网络体系结构，该体系结构能够充分利用极化热面部信息来训练深层模型，表现优于现有的交叉热-可见面部识别算法的最新模型。

深度交叉极化热-可见光人脸识别