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A pattern recognition system is described which is capable of identifying human faces from their full profile silhouettes. Each silhouette is preprocessed to remove noise, smooth edges, and extract the front edge. The processed silhouettes are then represented by a 12-dimensional feature vector, the components of which are obtained by a circular autocorrelation function. Using a weighted k-nearest neighbor decision rule it is shown that a recognition accuracy of 90 percent is attainable in a ten-class problem. An adaptive training procedure is also described which is used for setting up the authority files. This training procedure appears to identify those feature vectors representing a class which are either most important, from an information content point of view, or are observed most often. Finally, a comparison is made between the recognition accuracy obtained using circular autocorrelation features and moment invariant features. It is shown that the former outperforms, in this problem, the latter. The system is also compared to human observers with the result that the system performs no worse than the human observers.