Abstract
A new approach for personal identification using hand geometry based upon geometrical and shape features is presented. We propose a new pegless hand geometry verification system where the users are free to put their hand in arbitrary fashion. A Linear Discirminant Analysis if applied to the raw data in order to perform a best clustering of the feature space. The combination of three different neural network classifiers (unsupervised SOM, supervised SOM and LVQ) gives 0.35% FAR and 0.15% FRR. The method has been tested on a large size database of 1400 images for training and 1400 for test from 280 individuals suitable for medium and low security applications.
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References
Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Trans. on Circuits and Systems for Video Technology 14(1), 4–20 (2004)
Sanchez-Reillo, R., Sanchez-Avila, C., Gonzalez-Marcos, A.: Biometric Identification through Hand Geometry Measurements. IEEE Trans. on Pattern Analysis and Machine Recognition 22(10), 1169–1171 (2000)
Kumar, A., Wong, D.C.M., Shen, H.C., Jain, A.K.: Personal Verification using Palmprint and Hand Geometry Biometrics. In: Proceedings of the fourth International Conference on audio- and video-based biometric person authentication (2003)
Jain, A.K., Ross, A., Pankanti, S.: A Prototype Hand Geometry-based Verification System. In: Proc. of the 2nd International Conference on Audio- and Video-based Biometric Person Authentication, pp. 166–171 (1999)
http://visgraph.cs.ust.hk/biometrics/Visgraph_web/index.html
Bulatov, Y., Jambawalikar, S., Kumar, P., Sethia, S.: Hand Recognition Using Geometric Classifiers. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 753–759. Springer, Heidelberg (2004)
Jain, A.K., Duta, N.: Deformable matching of hand shapes for verification. In: Proceedings of International Conference on Image Processing (1999)
Oden, C., Ercil, A., Kirmizita, H., Buke, B.: Hand recognition using implicit polynomials and geometric feature. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 336–341. Springer, Heidelberg (2001)
http://www.eng.buffalo.edu/~ssc5/research/papers/biometric_hardening.pdf
Wong, A.L.N., Shi, P.: Peg-Free Hand geometry Recognition Using Hierarchical Geometry and Shape Matching. In: IAPR Workshop on Machine Vision Applications, pp. 281–284 (2002)
Otsu, N.: A threshold selection method from grey-scale histogram. IEEE Trans. Syst., Man, Cybern. 8, 62–66 (1978)
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Martínez, F., Orrite, C., Herrero, E. (2005). Biometric Hand Recognition Using Neural Networks. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_143
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DOI: https://doi.org/10.1007/11494669_143
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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