Abstract
This paper presents a novel method for gender recognition through anthropometric hand information. From a visual hand database of a hundred users and distributed in an unbalanced way, contains more men than women. It is designed a simple method to get some length and width measurements from the hand. This information has been passed through a quadratic discriminant classifier called Biometric Dispersion Matcher (BDM) that provides relevant information. In a first step, a discriminative threshold is applied in order to discard those measures which do not have enough information for gender recognition. In a second step, it provides a vector of the main measures. And, finally, it achieves performance rates from 95%, with a train data set of only 18 men and 9 women, to 98%, with a higher training data set.
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© 2013 Springer-Verlag Berlin Heidelberg
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Font-Aragones, X., Faundez-Zanuy, M. (2013). Hand-Based Gender Recognition Using Biometric Dispersion Matcher. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_37
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DOI: https://doi.org/10.1007/978-3-642-35467-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35466-3
Online ISBN: 978-3-642-35467-0
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