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Face Recognition: Features Versus Templates
October 1993 (vol. 15 no. 10)
pp. 1042-1052

Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching, are presented. The results obtained for the testing sets show about 90% correct recognition using geometrical features and perfect recognition using template matching.

[1] R. J. Baron, "Mechanisms of human facial recognition,"Int. J. Man Machine Studies, vol. 15, pp. 137-178, 1981.
[2] M. Bichsel, "Strategies of robust object recognition for the identification of human faces," Ph.D. thesis, Eidgenossischen Technischen Hochschule, Zurich, 1991.
[3] W. W. Bledsoe, "Man-machine facial recognition," Tech. Rep. PRI:22, Panoramic Res. Inc., Palo Alto, CA, 1966.
[4] R. Brunelli, "Edge projections for facial feature extraction," Tech. Rep. 9009-12, IRST, 1990.
[5] R. Brunelli, "Face recognition: Dynamic programming for the detection of face outline," Tech. Rep. 9104-06, IRST, 1991.
[6] R. Brunelli and T. Poggio, "Hyperbf networks for real object recognition," inProc. 12th IJCAI, (Sidney), 1991.
[7] R. Brunelli and T. Poggio, "Hyperbf networks for gender classification," inProc. DARPA Image Understanding Workshop, 1992.
[8] J. Buhmann, J. Lange, and C. von der Malsburg, "Distortion invariant object recognition by matching hierarchically labeled graphs," inProc. IJCNN Int. Conf. Neural Networks, Washington, DC, IEEE, 1989, pp. I 155-159.
[9] D. J. Burr, "Elastic matching of line drawings,"IEEE Trans. Patt. Anal. Machine Intell., vol. 3, no. 6, pp. 708-713, 1981.
[10] P. Burt, "Smart sensing within a pyramid vision machine,"Proc. IEEE, vol. 76, no. 8, pp. 1006-1015, 1988.
[11] H. Chan and W. W. Bledsoe, "A man-machine facial recognition system: Some preliminary results," Tech. Rep., Panoramic Res. Inc., Palo Alto, CA, 1965.
[12] G. Cottrell and M. Fleming, "Face recognition using unsupervised feature extraction," inProc. Int. Neural Network Conf., 1990.
[13] I. Craw, H. Ellis, and J. R. Lishman, "Automatic extraction of face features,"Patt. Recogn. Lett., vol. 5, pp. 183-187, Feb. 1987.
[14] K. Fukunaga,Introduction to Statistical Pattern Recognition. New York: Academic, 1972.
[15] A. J. Goldstein, L. D. Harmon, and A. B. Lesk, "Identification of human faces,"Proc. IEEE, vol. 59, p. 748, 1971.
[16] B. A. Golomb, D. T. Lawrence, and T. J. Sejnowski, "Sexnet: A neural network identifies sex from human faces," inAdvances in Neural Information Processing Systems 3, 1991, pp. 572-577.
[17] Z.-Q. Hong, "Algebraic feature extraction of image for recognition,"Patt. Recogn., vol. 24, no. 3, pp. 211-219, 1991.
[18] T. Kanade, "Picture processing by computer complex and recognition of human faces," Tech. Rep., Kyoto Univ., Dept. Inform. Sci., 1973.
[19] Y. Kaya and K. Kobayashi, "A basic study on human face recognition," inFrontiers of Pattern Recognition(S. Watanabe, Ed.). 1972, p. 265.
[20] M. Kirby and L. Sirovich, "Application of the Karhunen-Loeve procedure for the characterization of human faces,"IEEE Trans. Patt. Anal. Machine Intell., vol. 12, no. 1, pp. 103-108, 1990.
[21] Y. Lee, "Handwritten digit recognition using k nearest-neighbor, radial basis functions and backpropagation neural networks,"Neural Comput., vol. 3, no. 3, 1991.
[22] O. Nakamura, S. Mathur, and T. Minami, "Identification of human faces based on isodensity maps,"Patt. Recogn., vol. 24, no. 3, pp. 263-272, 1991.
[23] T. Poggio, "A theory of how the brain might work," inCold Spring Harbor Symp. Quant. Biol., 1990, pp. 899-909, vol. LV.
[24] T. Poggio, "3d object recognition and prototypes: One 2d view may be sufficient," Tech. Rep. 9107-02, IRST, 1991.
[25] T. Poggio and S. Edelman, "A network that learns to recognize three-dimensional objects,"Nature, vol. 343, no. 6225, pp. 1-3, 1990.
[26] T. Poggio and F. Girosi, "A theory for approximation and learning," AI Memo 1140, MIT, July 1989.
[27] T. Poggio and F. Girosi, "Networks for approximation and learning,"Proc. IEEE, vol. 78, pp. 1481-1497, Sept. 1990.
[28] T. Poggio and L. Stringa, "A project for an intelligent system: Vision and Learning,"Int. J. Quantum Chem., vol. 42, pp. 727-739, 1992.
[29] J. Sergent, "Structural processing of faces," inHandbook of Research on Face Processing(A. W. Young and H. D. Ellis, Eds.). Amsterdam: North-Holland, 1989.
[30] M. Dalla Serra and R. Brunelli, "On the use of the Karhunen-Loeve expansion for face recognition," Tech. Rep. 9206-04, IRST, 1992.
[31] L. Stringa, "Automatic face recognition using directional derivatives," Tech. Rep. 9205-04, IRST, 1991.
[32] L. Stringa, "Eyes detection for face recognition," Tech. Rep. 9203-07, IRST, 1991.
[33] M. Turk and A. Pentland, "Eigenfaces for recognition,"J. Cognitive Neurosci., vol. 3, no. 1, pp. 71-86, 1991.
[34] H. Voorhees, "Finding texture boundaries in images," Tech. Rep. AI-TR 968, Mass. Inst. Tecnol. Artificial Intell. Lab., 1987.
[35] A. W. Young and H. D. Ellis, Eds.,Handbook of Research on Face Processing. Amsterdam: North-Holland, 1989.
[36] A. L. Yuille, "Deformable templates for face recognition,"J. Cognitive Neurosci., vol. 3, no. 1, pp. 59-70, 1991.

Index Terms:
feature-based recognition; template-based recognition; nose length; computer recognition; geometrical features; nose width; mouth position; chin shape; almost-gray-level template matching; face recognition
R. Brunelli, T. Poggio, "Face Recognition: Features Versus Templates," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042-1052, Oct. 1993, doi:10.1109/34.254061
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