ABSTRACT
Biometrics has received a lot of attention during the past few years from both the academic and business communities. It has emerged as a preferred alternative to traditional forms of identication, like card IDs, which are not embedded into one’s physical characteristics. Research into several biometric modalities, including face, ngerprint, iris, and retina recognition, has produced varying degrees of success [1]. Face recognition stands as the most appealing modality, since it is the natural mode of identification among humans and is totally unobtrusive. At the same time, however, it is one of the most challenging modalities [2]. Research into face recognition has been biased toward the visible spectrum for a variety of reasons. Among those is the availability and low cost of visible band cameras and the undeniable fact that face recognition is one of the primary activities of the human visual system. Machine recognition of human faces, however, has proven more problematic than the seemingly effortless face recognition performed by humans. e major culprit is light variability, which is prevalent in the visible spectrum owing to the reflective nature of incident light in this band. Secondary problems are associated with the difficulty of detecting facial disguises [3].
