It is difficult to have a dependable face recognition system when only a single (or a few) image(s) per subject is used in the training set. Due to significant natural facial variations, e.g., facial expression, and appearances due to lighting condition, and head pose (even when dealing with “frontal view”), the use of a collection of images covering these variations and appearances will be of great help. We show that the expansion of the training set, by careful construction of synthetic images that capture all or most of the desired appearances can significantly improve the performance, especially when only one (or several, but very similar) real image(s) of a given individual are available. To have a better understanding of the issues involved, we address an inherently simpler problem, i.e. face/identity recognition/verification using only one eye and its associated eyebrow. We will particularly emphasize the synthesis and automation issues. Moreover, our experimental results indicate that the eye is rich in discriminative information, perhaps providing more information than what is normally utilized by humans. This wealth of information, however, can be exploited by machines for close-up images. Finally, we speculate that a similar improvement can be achieved when the training set is enriched with carefully generated synthetic images of the entire face.
KeywordsFace Recognition Real Image Lower eyeLid Synthetic Image Head Tilt
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