Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79338
Title: Multi-Siamese networks to accurately match contactless to contact-based fingerprint images
Authors: Lin, C 
Kumar, A 
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: IEEE International Joint Conference on Biometrics, IJCB 2017, 2018, v. 2018-January, p. 277-285 How to cite?
Abstract: Contactless 2D fingerprint identification is more hygienic, and enables deformation free imaging for higher accuracy. Success of such emerging contactless fingerprint technologies requires advanced capabilities to accurately match such fingerprint images with the conventional fingerprint databases which have been developed and deployed in last two decades. Convolutional neural networks have shown remarkable success for the face recognition problem. However, there has been very few attempts to develop CNN-based methods to address challenges in fingerprint identification problems. This paper proposes a multi-Siamese CNN architecture for accurately matching contactless and contact-based fingerprint images. In addition to the fingerprint images, hand-crafted fingerprint features, e.g. minutiae and core point, are also incorporated into the proposed architecture. This multi-Siamese CNN is trained using the fingerprint images and extracted features. Therefore, a more robust deep fingerprint representation is formed from the concatenation of deep feature vectors generated from multi-networks. In order to demonstrate the effectiveness of the proposed approach, a publicly available database consisting of contact-based and respective contactless finger-prints is utilized. The experimental evaluations presented in this paper achieve outperforming results, over other CNN-based methods and the traditional fingerprint cross matching methods, and validate our approach.
Description: 2017 IEEE International Joint Conference on Biometrics, IJCB 2017, Denver, United States, 1-4 October 2017
URI: http://hdl.handle.net/10397/79338
ISBN: 9781538611241
DOI: 10.1109/BTAS.2017.8272708
Appears in Collections:Conference Paper

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