Recent Advances in Heterogeneous Face Recognition (HFR): Infrared-to-Visible Matching
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This tutorial will address the recent advancements for the emerging research area of heterogeneous face recognition (HFR) from mainly an academic perspective but with insights from government and industry, which seeks to match facial probe imagery acquired in one modality against a gallery database of facial images acquired in another modality. HFR has strong potential to provide new capabilities for law enforcement, the military, and the intelligence community. In this tutorial, we will focus on infrared-to-visible face recognition for low- light and nighttime applications, discussing feature extraction techniques, regression methods, and classification algorithms for matching infrared imagery to gallery databases containing only visible imagery. We will also present recent advances in exploiting sensor technology such as polarimetric imaging for HFR and discuss new algorithms such as generative adversarial network-based approaches for HFR.