Research Article
Robust Fingerprint Matching Based on Convolutional Neural Networks
@INPROCEEDINGS{10.1007/978-3-319-90775-8_5, author={Yanming Zhu and Xuefei Yin and Jiankun Hu}, title={Robust Fingerprint Matching Based on Convolutional Neural Networks}, proceedings={Mobile Networks and Management. 9th International Conference, MONAMI 2017, Melbourne, Australia, December 13-15, 2017, Proceedings}, proceedings_a={MONAMI}, year={2018}, month={5}, keywords={Fingerprint matching Convolutional Neural Networks Fingerprint pairs Relational features Deep learning}, doi={10.1007/978-3-319-90775-8_5} }- Yanming Zhu
Xuefei Yin
Jiankun Hu
Year: 2018
Robust Fingerprint Matching Based on Convolutional Neural Networks
MONAMI
Springer
DOI: 10.1007/978-3-319-90775-8_5
Abstract
Fingerprint has been widely used in biometric authentication systems due to its uniqueness and consistency. Despite tremendous progress made in automatic fingerprint identification systems (AFIS), highly efficient and accurate fingerprint matching remains a critical challenge. In this paper, we propose a novel fingerprint matching method based on Convolutional Neural Networks (ConvNets). The fingerprint matching problem is formulated as a classification system, in which an elaborately designed ConvNets is learned to classify each fingerprint pair as a match or not. A key contribution of this work is to directly learn relational features, which indicate identity similarities, from raw pixels of fingerprint pairs. In order to achieve robustness and characterize the similarities comprehensively, incomplete and partial fingerprint pairs were taken into account to extract complementary features. Experimental results on FVC2002 database demonstrate the high performance of the proposed method in terms of both false acceptance rate (FAR) and false rejection rate (FRR). Thanks to the robustness of feature extraction, the proposed method is applicable of incomplete and partial fingerprint matching.