Optimal feature learning and discriminative framework for polarimetric thermal to visible face recognition

A face recognition system capable of day- and night-time operation is highly desirable for surveillance and reconnaissance. Polarimetric thermal imaging is ideal for such applications, as it acquires emitted radiation from skin tissue. However, polarimetric thermal facial imagery must be matched to visible face images for interoperability with existing biometric databases. This work proposes a novel framework for polarimetric thermal-to-visible face recognition, where polarimetric features are optimally combined to facilitate training of a discriminant classifier. We evaluate its performance on imagery collected under different expressions and at different ranges, and compare with recent deep perceptual mapping, coupled neural network, and partial least squares techniques for cross-spectrum face matching.