  In video based face recognition, face images are typically captured over
multiple frames in uncontrolled conditions, where head pose, illumination,
shadowing, motion blur and focus change over the sequence. Additionally,
inaccuracies in face localisation can also introduce scale and alignment
variations. Using all face images, including images of poor quality, can
actually degrade face recognition performance. While one solution it to use
only the "best" subset of images, current face selection techniques are
incapable of simultaneously handling all of the abovementioned issues. We
propose an efficient patch-based face image quality assessment algorithm which
quantifies the similarity of a face image to a probabilistic face model,
representing an "ideal" face. Image characteristics that affect recognition are
taken into account, including variations in geometric alignment (shift,
rotation and scale), sharpness, head pose and cast shadows. Experiments on
FERET and PIE datasets show that the proposed algorithm is able to identify
images which are simultaneously the most frontal, aligned, sharp and well
illuminated. Further experiments on a new video surveillance dataset (termed
ChokePoint) show that the proposed method provides better face subsets than
existing face selection techniques, leading to significant improvements in
recognition accuracy.
