基于卷积神经网络的儿童识别系统使用非接触式指纹
Kanchana Rajaram1, N G Bhuvaneswari Amma2, S Selvakumar3,4
1Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu 603 110 India.
International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management
|June 26, 2023
概括
本研究介绍了Child-CLEF,这是一个用于使用卷积神经网络 (CNN) 的儿童的非接触式指纹识别系统. 该系统提高了图像质量,并提取了用于准确识别的特征,优于现有方法.
科学领域:
- 生物识别和模式识别技术
- 计算机视觉和图像处理
- 人工智能在安全方面的应用
背景情况:
- 指纹识别对于安全至关重要,但儿童未成熟的指纹和难以获取图像存在挑战.
- 随着COVID-19的爆发,人们越来越需要无接触式生物识别解决方案,尤其是儿童.
- 现有系统正在努力应对儿科指纹数据的独特挑战.
研究的目的:
- 为儿童开发一个强大而准确的无接触指纹识别系统.
- 解决儿科生物识别传统指纹识别方法的局限性.
- 利用深度学习来增强儿童指纹识别.
主要方法:
- 一个基于卷积神经网络 (CNN) 的系统,Child-CLEF,是使用CLCF数据集开发的.
- 采用混合图像增强技术来提高获得的指纹图像的质量.
- 使用儿童-CLEF网络模型提取微小的特征,然后通过匹配算法进行识别.
主要成果:
- 与现有的指纹识别系统相比,Child-CLEF系统表现出卓越的性能.
- 该系统在自我捕获和公共数据集上实现了高精度和低等错误率 (EER).
- 使用手机进行非接触式采集,对儿童的指纹有效.
结论:
- 拟议的儿童-CLEF系统为儿童的无接触指纹识别提供了一个有希望的解决方案.
- 该研究验证了CNN和图像增强用于儿科生物识别的有效性.
- 这项研究有助于为儿童提供安全和非侵入性识别方法,特别是在敏感的环境中.
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