Efficient integration of thermal technology in facial image processing through interspectral synthesis

Thermal imaging technology has significantly evolved during the last couple of decades, mostly thanks to thermal cameras having become more affordable and user friendly. However, and given that the exploration of thermal imagery is reasonably new, only a few public databases are available to the research community. This limitation consequently prevents the impact of deep learning technologies from generating improved and reliable face biometric systems that operate in the thermal spectrum. A possible solution relates to the development of technologies that bridge the gap between the visible and thermal spectrum. In attempting to respond to this necessity, the research presented in this dissertation aims to explore interspectral synthesis as a direction for efficient and prompt integration of thermal technology in already deployed face biometric systems. As a first contribution, a new database, containing paired visible and thermal face images acquired simultaneously, was collected and made publicly available to foster research in thermal face image processing. Motivated by the need for fast and straightforward integration into existing face recognition systems, a set of contributions consisted of proposing a cross-spectrum face recognition framework based on a novel approach of thermal-to-visible face synthesis in order to estimate the visible face from the thermal input. Contributions consisting of exploring interspectral synthesis from visible to thermal spectrum for facial image processing tasks related to, but different than face recognition, are also presented including facial landmark detection and face biometric spoofing in the thermal spectrum.