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Computer Recognition of Facial Profiles
OHIO STATE UNIV COLUMBUS DEPT OF ELECTRICAL ENGINEERING
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A system for the recognition of human faces from full profile silhouettes is described. The system is adaptively trained using a novel stack- oriented training procedure which is shown to be quite effective in identifying those feature vectors which are of most importance in the recognition process. Thus the training procedure generally produces authority files having a small number of entries. The feature vectors used are generated from a normalized autocorrelation function expressed in polar coordinates. These feature vectors are shown to be more effective in the recognition process than are the moment invariant features, at least for this problem. Experiments are described in which the system attains a recognition accuracy of 90 in a 10 class problem using 12-dimensional circular autocorrelation feature vectors. It is shown, by further experiments, that these results are no worse than a human observers accuracy.
APPROVED FOR PUBLIC RELEASE