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Face Recognition System Using Local Autocorrelations and Multiscale Integration
October 1996 (vol. 18 no. 10)
pp. 1024-1028

Abstract—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.

[1] A. Samal and P.A. Iyengar,“Automatic recognition and analysis of human faces and facial expressions: A survey,” Pattern Recognition, vol. 25, no. 1, pp. 65-77, 1992.
[2] D. Valentin, H. Abdi, A.J. O'Toole, and G.W. Cottrell, "Connectionist Models of Face Processing: A Survey," Pattern Recognition, vol. 27, pp. 1,209-1,230, 1994.
[3] R. Brunelli and T. Poggio, "Face Recognition: Features vs. Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1,042-1,053, Oct. 1993.
[4] M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[5] A.L. Yuille, "Deformable Templates for Face Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 59-70, 1991.
[6] M. Lades, J.C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R.P. Wurtz, and W. Konen, “Distortion Invariant Object Recognition in the Dynamic Link Architecture,” IEEE Trans. Computers, vol. 42, no. 3, pp. 300-311, Mar. 1993.
[7] G.W. Cottrell and M.K. Fleming, "Categorisation of Faces Using Unsupervised Feature Extraction," Proc. Int'l Conf. Neural Networks,San Diego, vol. 2, pp. 65-70, 1990.
[8] J.L. Perry and J.M. Carney, "Human Face Recognition Using a Multilayer Perceptron," IJCNN Washington D.C., pp. 413-416, 1990.
[9] H. Bouattour, F. Fogelman-Soulié, and E. Viennet, "Solving the Human Face Recognition Task Using Neural Nets," Proc. Artificial Neural Networks, I. Aleksander and J. Taylor, eds., pp. 1,595-1,598.Amsterdam: North-Holland, 1992.
[10] M. Bichsel, "Strategies of Robust Object Recognition for Automatic Identification of Human Faces," PhD Thesis No. 9467, Eidgenössischen Technischen Hochschule Zürich, 1991.
[11] F. Goudail, E. Lange, T. Iwamoto, K. Kyuma, and N. Otsu, "Fast Face Recognition Method using High Order Autocorrelations," Proc. IJCNN Int'l Conf. Neural Networks,Nagoya, Japan, pp. 1,297-1,300, 1993.
[12] N. Otsu and T. Kurita, "A New Scheme for Practical, Flexible and Intelligent Vision Systems," Proc. IAPR Workshop on Computer Vision—Special Hardware and Industrial Applications, pp. 431-435,Tokyo, 1988.
[13] T. Kurita, N. Otsu, and T. Sato, "A Face Recognition Method Using Higher Order Local Autocorrelation and Multivariate Analysis," Proc. 11th IAPR Int'l Conf. Pattern Recognition, pp. 213-216, 1992.
[14] K. Fukunaga, Introduction to Statistical Pattern Recognition, second edition. Academic Press, 1990.
[15] F. Goudail, "Large Scale Face Recognition Using Local Autocorrelations," Rapport de stage de fin d'études, École Supérieure d'Optique, Amagasaki, Japan, 1993.
[16] G. Choquet, "Theory of Capacities," Annales de l'Institute Fourier., vol. 5, pp. 131-295, 1953.
[17] T.D. Arbuckle, E. Lange, T. Iwamoto, N. Otsu, and K. Kyuma, "Fuzzy Information Fusion in a Face Recognition System," Int'l J. Uncertainty, Fuzziness, and Knowledge-Based Systems, submitted.
[18] D. Hammerstrom, "A VLSI Architecture for High-Performance, Low-Cost, On-Chip Learning," Proc. Int'l. Joint Conf. Neural Networks, IEEE Neural Networks Council, Ann Arbor, Mich., 1990, pp. II-537-II-543.
[19] U. Ramacher, J. Beichter, W. Raab, J. Anlauf, N. Bruls, M. Hachmann, and M. Wesseling, "Design of a First Generation Neurocomputer," VLSI Design of Neural Networks, Kluwer Academic, 1991.
[20] Y. Kondo, Y. Koshiba, Y. Arima, M. Murasaki, T. Yamada, H. Amishiro, H. Shinohara, and H. Mori, "1.2 GFLOPS Neural Network Chip Exhibiting Fast Convergence," Proc. Int'l Solid-State Circuits Conf., pp. 218-219,San Francisco, 1994.
[21] E. Lange, Y. Nitta, and K. Kyuma, "Optical Neural Chips," IEEE Micro, vol. 14, no. 6, pp. 29-41, 1994.
[22] E. Lange, T. Arbuckle, F. Goudail, T. Iwamoto, K. Kyuma, and N. Otsu, "Towards a Vision Processor in Neural Architecture," Proc. Int'l Conf. Artificial Neural Networks, pp. 287-295,Paris, Oct. 1995.
[23] E. Lange, Y. Nitta, and K. Kyuma, "Direct Image Processing Using Artificial Retina Chips," Trends in Optics: Research Developments and Applications, A. Consortini, ed., pp. 63-82. Academic Press, 1996.
[24] M. Bischel and A. Pentland,“Human face recognition and face image set’s topology,” CVGIP: Image Understanding, vol. 59, no. 2, pp. 54-261, Mar. 1994.
[25] Y. Kaya and K. Kobayashi, "A Basic Study on Human Face Recognition," Frontiers of Pattern Recognition, S. Watanabe, ed., 1972.

Index Terms:
Classification, face recognition, autocorrelation, object recognition, shift invariant feature extraction, multiresolution image analysis.
Citation:
François Goudail, Eberhard Lange, Takashi Iwamoto, Kazuo Kyuma, Nobuyuki Otsu, "Face Recognition System Using Local Autocorrelations and Multiscale Integration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 1024-1028, Oct. 1996, doi:10.1109/34.541411
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