Abstract
In the last decade, many recognition and authentication systems based on biometric mesaurements have been proposed. Still algorithms based on face images are quite appealing for the possibility to easy adapt and taylor a system to many application domains.
A system for personal identity verification and also recognition is presented. The core engine is a standard correlation-based matcher performed on iconic representations of face images. Two data sets are used to validate the performances of the whole system (from data acquisition to recognition): the former is a standard “academic” database (with known acquisition parameters) similar to the FERET image set, the latter is an “industrial” data set acquired in a real application scenario. Through standard statistical tests of the recognition results obtained from the two data sets the actual physical limits of the pattern matcher are clearly shown. Successively also other aspects are taken into account, related to the feature space, allowing to greatly improve the system performance reaching almost 100% correct recognition.
Several hints for the development of new techniques for identity verification are also suggested.
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References
B. Moghaddam and A. Pentland. Face recognition using view-based and modular eigenspaces. In SPIE Int. Conf. on Automatic Systems for the Identification and Inspection of Humans, volume 2277, Boston (MA), 1994.
P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on PAMI, PAMI-19(7):711–20, 1997.
S.J. McKenna and S. Gong. Real-time pose estimation. Real-Time Imaging, 4(5):333–47, 1998.
T.F. Cootes, G.J. Edwards, and C.J. Taylor. Active appearance models. In Proc. of 5th European Conference on Computer Vision, pages 484–98, Freiburg (Germany), 1998. Springer Verlag.
C. Beumier and M. Acheroy. Automatic 3d face authentication. Image and Vision Computing, 18(4), 2000.
M. Proesmans and L. Van Gool. One shot 3d-shape and texture acquisition of facial data. In Audio and Video based Person Authentication-AVBPA97. Springer, 1997.
R. Brunelli and T. Poggio. Face recognition: Features versus templates. IEEE Trans. on PAMI, PAMI-15(10):1042–1052, 1993.
I. Biederman. Human image understanding: Recent research and theory. CVGIP, 32:29–73, 1985.
L. Wiskott, J.M. Fellous, N. Kruger, and C. von der Malsburg. Face recognition by elastic bunch graph matching. IEEE Trans. on PAMI, PAMI-19:775–779, 1997.
M. Tistarelli. Active/space-variant object recognition. Image and Vision Computing, 13(3):215–226, 1995.
P. J. Phillips, H. Moon, P. Rauss, and S. A. Rizvi. The feret september 1996 database and evaluation procedure. In Audio and Video based Person Authentication-AVBPA97, pages 395–402. IAPR, Springer, 1997.
M. Tistarelli and E. Grosso. Active vision-based face authentication. Image and Vision Computing, 18(4):299–314, 2000.
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© 2002 Springer-Verlag Berlin Heidelberg
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Tistarelli, M., Lagorio, A., Grosso, E. (2002). Understanding Iconic Image-Based Face Biometrics. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds) Biometric Authentication. BioAW 2002. Lecture Notes in Computer Science, vol 2359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47917-1_3
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DOI: https://doi.org/10.1007/3-540-47917-1_3
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