强化深度学习分类器的绩效评估,用于人员识别和性别分类
Vasu Krishna Suravarapu1, Hemprasad Yashwant Patil2
1School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Scientific reports
|August 1, 2025
概括
本研究引入了一种增强的深度学习分类器 (EDLC),用于使用周期性图像进行准确的人身识别和性别分类. 这种新的方法显著提高了生物识别系统的准确性和效率.
科学领域:
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
- 人工智能的人工智能
背景情况:
- 周眼生物识别对于个人身份验证至关重要.
- 现有系统在准确性,过度装配和计算效率方面面临挑战.
- 深度学习提供了潜力,但需要专门的架构来进行周期性识别.
研究的目的:
- 提出一种增强的深度学习分类器 (EDLC) 范式,用于使用周期性图像进行人身识别和性别分类.
- 解决当前生物识别系统的局限性,包括准确性和效率.
- 引入用于感兴趣区域 (ROI) 提取和特征表示的新方法.
主要方法:
- 一种新的六角形的ROI提取技术用于periocular区域.
- 使用拉普拉斯变换的特征提取.
- 实现了三个定制EDLC:扩展轴心注意力CNN,基于自我光谱注意力的关系变压器网络 (SSA-RTNet) 和参数化的超复杂卷积罗网络.
- 使用自适应性coati优化算法进行超参数优化.
主要成果:
- 在人身识别方面,SSA-RTNet实现了最高准确率99.8% (UBIPr) 和99.67% (UFPR).
- 对于性别分类,SSA-RTNet的准确率为98.4% (UBIPr) 和99.68% (UFPR).
- 改进后的模型比现有的竞争模型有了显著的改进.
结论:
- 拟议的EDLC范式,特别是SSA-RTNet,为周期性生物识别系统提供了重大进步.
- 新的ROI提取和特征提取方法有助于提高性能.
- 该研究强调了先进的深度学习技术在强大的个人识别和性别分类方面的潜力.
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