Title: Research on the application of convolutional-deep neural networks in parallel fingerprint minutiae matching
Authors: SuHua Wang; MingJun Cheng; ZhiQiang Ma; XiaoXin Sun
Addresses: College of Environment, Northeast Normal University, Changchun 130117, China; College of Humanities and Sciences, Northeast Normal University, Changchun 130117, China; Jilin Kesi Information Technology Co., Ltd., Changchun 130117, China ' Jilin Kesi Information Technology Co., Ltd., Changchun 130117, China ' College of Humanities and Sciences, Northeast Normal University, Changchun 130117, China ' College of Information Science and Technology, Northeast Normal University, Changchun 130117, China
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.
Keywords: convolution network; deep neural network; fingerprint minutiae; parallel matching; minutiae matching.
International Journal of Biometrics, 2021 Vol.13 No.1, pp.96 - 113
Received: 01 Feb 2020
Accepted: 09 Apr 2020
Published online: 05 Jan 2021 *