  Interest in thermal to visible face recognition has grown significantly over
the last decade due to advancements in thermal infrared cameras and analytics
beyond the visible spectrum. Despite large discrepancies between thermal and
visible spectra, existing approaches bridge domain gaps by either synthesizing
visible faces from thermal faces or by learning the cross-spectrum image
representations. These approaches typically work well with frontal facial
imagery collected at varying ranges and expressions, but exhibit significantly
reduced performance when matching thermal faces with varying poses to frontal
visible faces. We propose a novel Domain and Pose Invariant Framework that
simultaneously learns domain and pose invariant representations. Our proposed
framework is composed of modified networks for extracting the most correlated
intermediate representations from off-pose thermal and frontal visible face
imagery, a sub-network to jointly bridge domain and pose gaps, and a joint-loss
function comprised of cross-spectrum and pose-correction losses. We demonstrate
efficacy and advantages of the proposed method by evaluating on three
thermal-visible datasets: ARL Visible-to-Thermal Face, ARL Multimodal Face, and
Tufts Face. Although DPIF focuses on learning to match off-pose thermal to
frontal visible faces, we also show that DPIF enhances performance when
matching frontal thermal face images to frontal visible face images.
