# Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition

## TL;DR

This paper introduces an attribute-guided deep learning framework using GANs to improve polarimetric thermal-to-visible face recognition by leveraging facial attributes and multiple loss functions for discriminative feature learning.

## Contribution

It proposes a novel AGC-GAN architecture that incorporates facial attributes and perceptual loss to enhance thermal-to-visible face matching performance.

## Key findings

- Outperforms state-of-the-art models on polarimetric datasets
- Effectively leverages facial attributes for improved recognition
- Demonstrates the importance of combined loss functions in training

## Abstract

In this paper, we present an attribute-guided deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. The coupled framework contains two sub-networks, one dedicated to the visible spectrum and the second sub-network dedicated to the polarimetric thermal spectrum. Each sub-network is made of a generative adversarial network (GAN) architecture. We propose a novel Attribute-Guided Coupled Generative Adversarial Network (AGC-GAN) architecture which utilizes facial attributes to improve the thermal-to-visible face recognition performance. The proposed AGC-GAN exploits the facial attributes and leverages multiple loss functions in order to learn rich discriminative features in a common embedding subspace. To achieve a realistic photo reconstruction while preserving the discriminative information, we also add a perceptual loss term to the coupling loss function. An ablation study is performed to show the effectiveness of different loss functions for optimizing the proposed method. Moreover, the superiority of the model compared to the state-of-the-art models is demonstrated using polarimetric dataset.