Abstract
Hand recognition has received wide acceptance in many applications for automatic personal identification or verification in low to medium security systems. In this paper, we present a new approach for hand recognition based on abductive machine learning and hand geometric features. This approach is evaluated and compared to other learning algorithms including decision trees, support vector machines, and rule-based classifiers. Unlike other algorithms, the abductive learning approach builds simple polynomial neural network models by automatically selecting the most relevant features for each case. It also has acceptable accuracy with low false acceptance and false rejection rates. For the adopted dataset, the abductive learning approach has more than 98% overall accuracy, 1.67% average false rejection rate, and 0.088% average false acceptance rate.
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References
Yörük, E., Konukoglu, E., Sankur, B., Darbon, J.: Shape-based hand recognition. IEEE Transactions on Image Processing 15(7), 1803–1815 (2006)
Sidlauskas, D.P., Tamer, S.: Hand geometry recognition. In: Jain, A.K., Flynn, P., Ross, A.A. (eds.) Handbook of Biometrics, pp. 91–107. Springer US, Boston (2008)
Kumar, A., Wong, D.C.M., Shen, H.C., Jain, A.K.: Personal authentication using hand images. Pattern Recognition Letters 27(13), 1478–1486 (2006)
Nicolae, D.: A survey of biometric technology based on hand shape. Pattern Recognition 42(11), 2797–2806 (2009)
Pavesic, N., Ribaric, S., Ribaric, D.: Personal authentication using hand-geometry and palmprint features: the state of the art. In: Proc. Workshop on Biometrics, ICPR 2004 (2004)
AIM User’s Manual. AbTech Corporation, Charlottesville, VA (1990)
Anastasakis, L., Mort, N.: The development of self-organization techniques in modeling: A review of the group method of data handling (GMDH). In: Research Report, Department of Automatic Control and Systems Engineering, University of Sheffield (2001)
Farlow, S.J.: The GMDH algorithm. In: Farlow, S.J. (ed.) Self-Organizing Methods in Modeling: GMDH Type Algorithms, pp. 1–24. Marcel-Dekker, New York (1984)
Barron, A.R.: Predicted squared error: A criterion for automatic model selection. In: Farlow, S.J. (ed.) Self-Organizing Methods in Modeling: GMDH Type Algorithms, pp. 87–103. Marcel-Dekker, New York (1984)
Selvarajan, S., Palanisamy, V., Mathivanan, B.: Human identification and recognition system using more significant hand attributes. In: Proc. of International Conference on Computer and Communication Engineering, ICCCE (2008)
Kumar, A., Wong, D., Shen, H., Jain, A.: Personal verification using palmprint and hand geometry biometric. In: Proc. of 4th International Conference on Audio- and Video-Based Biometric Person Authentication (2003)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3 edn. Morgan Kaufmann (2011)
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El-Alfy, ES.M., Abdel-Aal, R.E., Baig, Z.A. (2012). Abductive Neural Network Modeling for Hand Recognition Using Geometric Features. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_72
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DOI: https://doi.org/10.1007/978-3-642-34478-7_72
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