  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.
