深度学习技术在指纹识别中的应用

doi:10.19678/j.issn.1000-3428.0068276

摘要/Abstract

摘要：

指纹识别是应用最早、使用最成熟的一项生物特征识别技术, 在民用领域的门禁考勤、移动支付以及刑侦领域检视嫌疑人线索等方面均有着广泛的应用。近年来, 深度学习技术给计算机视觉以及生物特征领域带来了深刻变革, 也给指纹研究人员提供了一种自动处理以及应用融合特征有效表示指纹的新方法, 在指纹识别的各个阶段均有着优异的效果。概述指纹识别的发展历史与应用背景, 阐述指纹识别图像预处理、特征提取以及指纹匹配3个阶段的主要处理流程, 分别对深度学习技术在不同阶段的应用现状进行归纳和总结, 比较不同深度神经网络在图像分割、图像增强、方向场估计、细节特征提取以及指纹匹配等具体环节的应用效果。最后, 分析当前指纹识别领域存在的一些问题与挑战, 并对构建公开指纹数据集、进行多尺度指纹特征提取以及训练端到端指纹识别模型等未来的发展方向进行展望。

关键词: 指纹识别, 深度学习, 卷积神经网络, 指纹自动识别系统, 指纹检验

Abstract:

Fingerprint recognition is one of the earliest and most mature biometric recognition technologies that is widely used in mobile payments, access control and attendance in the civilian field, and in criminal investigation to retrieve clues from suspects. Recently, deep learning technology has achieved excellent application results in the field of biometric recognition, and provided fingerprint researchers with new methods for automatic processing and the application of fusion features to effectively represent fingerprints, which have excellent application results at all stages of the fingerprint recognition process. This paper outlines the development history and application background of fingerprint recognition, expounds the main processing processes of the three stages of fingerprint recognition, which are image preprocessing, feature extraction, and fingerprint matching, summarizes the application status of deep learning technology in specific links at different stages, and compares the advantages and disadvantages of different deep neural networks in specific links, such as image segmentation, image enhancement, direction field estimation, minutiae extraction, and fingerprint matching. Finally, some of the current problems and challenges in the field of fingerprint recognition are analyzed, and future development directions, such as building public fingerprint datasets, multi-scale fingerprint feature extraction, and training end-to-end fingerprint recognition models, are prospected.

Key words: fingerprint recognition, deep learning, Convolutional Neural Network (CNN), automatic fingerprint recognition system, fingerprint examination

李硕, 赵朝阳, 屈音璇, 罗亚平. 深度学习技术在指纹识别中的应用[J]. 计算机工程, 2024, 50(12): 33-47.

LI Shuo, ZHAO Chaoyang, QU Yinxuan, LUO Yaping. Application of Deep Learning in Fingerprint Recognition[J]. Computer Engineering, 2024, 50(12): 33-47.

https://www.ecice06.com/CN/Y2024/V50/I12/33

图/表 17

图1 指纹识别流程

Fig.1 Fingerprint recognition procedure

图2 SegFinNet结构

Fig.2 The structure of SegFinNet

图3 增强网络与鉴别网络结构

Fig.3 Enhance network and authentication network structure

图4 RefNet结构

Fig.4 The structure of RefNet

图5 FCTP-Net网络结构与参数

Fig.5 The structure and parameters of FCTP-Net

图6 FingerNet网络结构

Fig.6 The structure of FingerNet

图7 MinutiaeNet网络结构

Fig.7 The structure of MinutiaeNet

图8 细节特征提取网络结构^[60]

Fig.8 Detail feature extraction network structure^[60]

图9 汗孔检测流程^[66]

Fig.9 Sweat hole detection process^[66]

图10 端到端的指纹检索系统处理流程^[72]

Fig.10 The process flow of end-to-end fingerprint retrieval system processing^[72]

图11 DeepPrint的指纹处理流程

Fig.11 DeepPrint′s fingerprint processing procedure

图12 AFR-Net网络结构

Fig.12 The structure of AFR-Net

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