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<p><b>Abstract</b>—In this paper we investigate the performance of a technique for face recognition based on the computation of 25 local autocorrelation coefficients. We use a large database of 11,600 frontal facial images of 116 persons, organized in training and test sets, for evaluation. Autocorrelation coefficients are computationally inexpensive, inherently shift-invariant and quite robust against changes in facial expression. We focus on the difficult problem of recognizing a large number of known human faces while rejecting other, unknown faces which lie quite close in pattern space. A multiresolution system achieves a recognition rate of 95%, while falsely accepting only 1.5% of unknown faces. It operates at a speed of about one face per second. Without rejection of unknown faces, we obtain a peak recognition rate of 99.9%. The good performance indicates that local autocorrelation coefficients have a surprisingly high information content.</p>
Classification, face recognition, autocorrelation, object recognition, shift invariant feature extraction, multiresolution image analysis.

F. Goudail, E. Lange, T. Iwamoto, N. Otsu and K. Kyuma, "Face Recognition System Using Local Autocorrelations and Multiscale Integration," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 18, no. , pp. 1024-1028, 1996.
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