The difficulty of face recognition (FR) systems to operate efficiently in diverse operational environments, e.g. day and night time, is aided by employing sensors covering different spectral bands (i.e. visible and infrared). Biometric practitioners have identified a framework of band-specific algorithms, which can contribute to both assessment and intervention. While these motions are proven to achieve improvement of identification performance, they traditionally result in solutions that typically fail to work efficiently across multiple spectrums. In this work, we designed and developed an efficient, fully automated, direct matching-based FR approach, that is designed to operate efficiently when face data is captured using either visible or passive infrared (IR) sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Our approach achieves a rank-1 identification rate of at least 99.43%, regardless of the spectrum of operation. This suggests that our approach results in better performance than other tested standard commercial and academic face-based matchers, on all spectral bands used. 
A spectral independent approach for physiological and geometric based face recognition in the visible, middle-wave and long-wave infrared bands