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.
