ZN-Face: A system for access control using automated face recognition
- 149 Downloads
We present a biometric access control device which is based on the identification of human faces. The system combines a console for semi-automated image acquisition with the necessary algorithms for face recognition. Facial features are stored in a relatively compact data format (1.6 kB). ZN-Face runs on a Pentium 90 without any special accelerator hardware where it performs image acquisition, face localization and identification in less than 3 seconds. ZN-Face not only allows robust identification of stored persons (despite changes in facial expression or size), but also reliable rejection of unknown persons. With an acceptance criterion which safely rejects all unknown persons we achieve an identification rate above 99% (FRR< 1%). The ZN Bochum GmbH has sold more than 100 licences to various institutions and companies, among them the Kremlin in Moscow. The ZN Bochum GmbH holds the relevant patents for ZN-Face.
KeywordsFace Recognition Facial Image False Acceptance Rate False Rejection Rate Face Recognition System
Unable to display preview. Download preview PDF.
- V. Bruce and M. Burton. Processing Images of Faces. ABLEX Publishing-Corporation, Norwood, NJ, 1992.Google Scholar
- T. Kanade. Picture Processing System by Computer Complex and Recognition of Human Faces. Unpublished Ph.D. thesis, Dept. of Information Science, Kyoto Univ., 1973.Google Scholar
- R. Brunelli and Toinaso Poggio. Face recognition: Features versus templates. Technical Report TR 9110–04, Istituto per la Ricerca Scientifica e Tecnologica, October 1992.Google Scholar
- J. M. Gilbert and Woodward Yang. A real-time face recognition system using custom VLSI hardware. In Proc. of Computer Architectures for Machine Perception Workshop, December 1993.Google Scholar
- M. Turk and A. Pentland. Face recognition using eigenfaces. In IEEE Proc. of CVPR, pages 586–591, Maui, Hawaii, June 1991.Google Scholar
- T. Kohonen, E. Oja, A. Korteka.nga.s, and K. Mäkisara. In Proc. Intl. Conf. on Cybernetics and Scociety, Washington D.C., 1977.Google Scholar
- G. Cottrell, P. Munro, and D. Zipser. Learning internal representations of grey scale images: An example of extensional programming. In Proc. Ninth Annual Cognitive Science Society Conference, Seattle, WA, 1987.Google Scholar
- B. A. Golomb, D. T. Lawrence, and T. J. Sejnowski. SEXNET: A neural network identifies sex from human faces. In D. S. Touretzky and R. Lippman, editors, Advances in Neural Information Processing Systems 3. Morgan Kaufmann, San Mateo, 1991.Google Scholar
- M. Bichsel and P. Seitz. Der elektronische Pförtner: Automatisches Erkennen und Identifizieren von menschlichen Gesichtern. In R.E. Grosskopf, editor, Mustererkennung 1990, 12. DAGM-Syinposium. Springer, 1990.Google Scholar