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Face recognition system using local autocorrelations and multiscale integration

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5 Author(s)
F. Goudail ; Lab. Signal et Image, Domaine Univ., France ; E. Lange ; T. Iwamoto ; K. Kyuma
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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

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:18 ,  Issue: 10 )