Deep Cross Polarimetric Thermal-to-visible Face Recognition

TL;DR

This paper introduces a deep coupled learning framework that effectively matches polarimetric thermal face images with visible face images, leveraging polarization data and large datasets to improve recognition accuracy.

Contribution

It presents the first polarimetric thermal face dataset and a novel deep neural network architecture that utilizes polarization information for thermal-to-visible face recognition.

Findings

Outperforms state-of-the-art models in cross-spectrum face recognition

Utilizes polarization data to enhance feature discrimination

First to train on a polarimetric thermal face dataset

Abstract

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.