Thermal-to-Visible Face Recognition
The motivation behind thermal face recognition (FR) is the need for enhanced intelligence gathering capabilities in darkness where active illumination is impractical and when surveillance with visible cameras is not feasible.
Infrared (IR) thermal cameras are important for night-time surveillance and security applications. They are especially useful in nighttime scenarios when the subject is far away from the camera. However, the acquired thermal face images have to be identified using the images from existing visible face databases. Therefore, cross-spectral face matching between the thermal and visible spectrum is a much desired capability.
In cross-modal face recognition, identifying a thermal probe image based on a visible face database is especially difficult because of the wide modality gap between thermal and visible physical phenomenology. In this project we address the cross-spectral (thermal vs. visible) and cross-distance (50 m, 100 m, and 150 m vs. 1 m standoff) face matching problem for night-time FR applications.
Previous research activities have mainly concentrated on extracting hand-crafted features (i.e., SIFT, SURF, HOG, LBP, wavelets, Gabor jets, kernel functions) by assuming that the two modalities share the same extracted features. However, the relationship between the two modalities is highly non-linear. In this project we will investigate non-linear mapping techniques based on deep neural networks (DNN) learning procedures to bridge the modality gap between visible-thermal spectrum while preserving the subject identity information. The nonlinear coupled DNN features will be used by a FR classifier.