Cross-Domain Identification for Thermal-to-Visible Face Recognition

• URL: http://arxiv.org/abs/2008.08473v1
• Date: Wed, 19 Aug 2020 14:24:04 GMT
• Title: Cross-Domain Identification for Thermal-to-Visible Face Recognition
• Authors: Cedric Nimpa Fondje, Shuowen Hu, Nathaniel J. Short, Benjamin S. Riggan
• Abstract summary: This paper proposes a novel domain adaptation framework that combines a new feature mapping sub-network with existing deep feature models. New cross-domain identity and domain invariance loss functions for thermal-to-visible face recognition alleviates the requirement for precisely co-registered and synchronized imagery. We analyze the viability of the proposed framework for more challenging tasks, such as non-frontal thermal-to-visible face recognition.
• Score: 6.224425156703344
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent advances in domain adaptation, especially those applied to heterogeneous facial recognition, typically rely upon restrictive Euclidean loss functions (e.g., $L_2$ norm) which perform best when images from two different domains (e.g., visible and thermal) are co-registered and temporally synchronized. This paper proposes a novel domain adaptation framework that combines a new feature mapping sub-network with existing deep feature models, which are based on modified network architectures (e.g., VGG16 or Resnet50). This framework is optimized by introducing new cross-domain identity and domain invariance loss functions for thermal-to-visible face recognition, which alleviates the requirement for precisely co-registered and synchronized imagery. We provide extensive analysis of both features and loss functions used, and compare the proposed domain adaptation framework with state-of-the-art feature based domain adaptation models on a difficult dataset containing facial imagery collected at varying ranges, poses, and expressions. Moreover, we analyze the viability of the proposed framework for more challenging tasks, such as non-frontal thermal-to-visible face recognition.

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