2 research outputs found
Cross-Domain Identification for Thermal-to-Visible Face Recognition
Recent advances in domain adaptation, especially those applied to
heterogeneous facial recognition, typically rely upon restrictive Euclidean
loss functions (e.g., 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