wuzhijian_2008的专栏

A key problem for face recognition has been accurate identification under variable illumination conditions. Conventional video cameras sense reflected light so that image grayvalues are a product of both intrinsic skin reflectivity and external incident illumination, thus obfuscating the intrinsic reflectivity of skin. Thermal emission from skin, on the other hand, is an intrinsic measurement that can be isolated from external illumination. We examine the invariance of Long-Wave InfraRed (LWIR) imagery with respect to different illumination conditions from the viewpoint of performance comparisons of two well-known face recognition algorithms applied to LWIR and visible imagery. We develop rigourous data collection protocols that formalize face recognition analysis for computer vision in the thermal IR.

Techniques for face recognition generally fall into global and local approaches, with the principal component analysis (PCA) being the most prominent global approach. This paper uses the PCA algorithm to study the comparison and combination of infrared and typical visible-light images for face recognition. This study examines the effects of lighting change, facial expression change and passage of time between the gallery image and probe image. Experimental results indicate that when there is substantial passage of time (greater than one week) between the gallery and probe images, recognition from typical visible-light images may outperform that from infrared images. Experimental results also indicate that the combination of the two generally outperforms either one alone. This is the only study that we know of to focus on the issue of how passage of time affects infrared face recognition.