Research on the application of convolutional-deep neural networks in parallel fingerprint minutiae matching
by SuHua Wang; MingJun Cheng; ZhiQiang Ma; XiaoXin Sun
International Journal of Biometrics (IJBM), Vol. 13, No. 1, 2021

Abstract: In order to overcome the problem of low throughput and time-consuming of traditional fingerprint minutiae matching methods, a new convolutional-deep neural network is proposed for parallel fingerprint minutiae matching. This method realises the preprocessing of the initial image through four steps: normalisation, image enhancement, parallel thinning and image segmentation. The convolutional-deep neural network is constructed from convolution kernel, convolution layer, pooling layer and full connection layer to extract minutiae of fingerprint image. Through feature minutiae matching, local matching and global matching, the matching results of fingerprint parallel nodes are obtained. The experimental results show that compared with the traditional matching method, the fingerprint matching throughput of convolutional-deep neural network is increased by 25%, and the matching time is saved by about 8 seconds.

Online publication date: Tue, 05-Jan-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com