Abstract
We propose a novel and efficient technique to extract individual fingerprints from a slap-image and identify them into their corresponding indices i.e. index, middle, ring or little finger of left/right hand. We pose the orientation of the hand to a classification problem, and present an approach based on Convolutional Neural Networks (ConvNets) to address the angle of the hand. Geometrical and spatial properties of hand are applied to split a single finger and detect the knuckle line. The proposed algorithm solves the challenges of segmentation like the large rotational angles of the hand and non-elliptical shape of components. Extensive experimental evaluations demonstrate the success of this approach.
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Acknowledgment
This work was funded by the Chinese National Natural Science Foundation (11331012, 11571014, 1731013).
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Tang, S., Qin, J., Liu, Y., Han, C., Guo, T. (2017). An Efficient Slap Fingerprint Segmentation Algorithm Based on Convnets and Knuckle Line. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_25
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DOI: https://doi.org/10.1007/978-3-319-69923-3_25
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