Thermal to Visible Synthesis of Face Images using Multiple Regions

TL;DR

This paper introduces a novel method for synthesizing visible face images from thermal images by leveraging multiple facial regions, improving cross-spectrum verification and facial landmark detection accuracy.

Contribution

The paper presents a new multi-region synthesis approach that enhances the quality of thermal-to-visible face image conversion for better recognition performance.

Findings

Improved cross-spectrum verification rates.

Enhanced facial landmark detection accuracy.

Effective use of global and local regions in synthesis.

Abstract

Synthesis of visible spectrum faces from thermal facial imagery is a promising approach for heterogeneous face recognition; enabling existing face recognition software trained on visible imagery to be leveraged, and allowing human analysts to verify cross-spectrum matches more effectively. We propose a new synthesis method to enhance the discriminative quality of synthesized visible face imagery by leveraging both global (e.g., entire face) and local regions (e.g., eyes, nose, and mouth). Here, each region provides (1) an independent representation for the corresponding area, and (2) additional regularization terms, which impact the overall quality of synthesized images. We analyze the effects of using multiple regions to synthesize a visible face image from a thermal face. We demonstrate that our approach improves cross-spectrum verification rates over recently published synthesis approaches. Moreover, using our synthesized imagery, we report the results on facial landmark detection-commonly used for image registration-which is a critical part of the face recognition process.