具有多功能质量过的CBAM-DenseNet:在小样本虹膜识别中提高准确性
Yongheng Pang1,2, Zishen Wang2, Nan Jiang2
1Shanghai Key Laboratory of Forensic Medicine and Key Laboratory of Forensic Science, Ministry of Justice, Shenyang, Liaoning, China.
Frontiers in artificial intelligence
|February 12, 2026
概括
传统的安全方法不足. 本研究引入了一种多功能融合虹膜识别方法,提高了信息时代安全认证的准确性和稳定性.
科学领域:
- 生物识别信息 生物识别信息
- 计算机科学 计算机科学
- 信息安全 信息安全
背景情况:
- 传统的密码和基于密钥的身份验证不足以满足现代信息安全需求.
- 虹膜识别提供了高安全性和独特性,但当前的方法遭受特征信息丢失.
- 在现有的虹膜识别技术中,单个特征的提取限制了识别的准确性.
研究的目的:
- 提出一种基于多功能融合的虹膜识别新方法.
- 为了提高虹膜识别系统的准确性和稳定性.
- 解决当前虹膜识别方法中单个特征提取的局限性.
主要方法:
- 实施了虹膜图像过的综合质量评估方案.
- 使用了改进的CAN网络,以有效消除图像噪声.
- 采用DenseNet用于虹膜特征提取,结合融合空间和注意力机制 (CBAM) 进行特征表达.
主要成果:
- 通过实验验验证了识别准确性的显著改进.
- 证明了拟议的虹膜识别方法的增强稳定性.
- 在小样本大小和公共虹膜数据库上实现了卓越的性能.
结论:
- 拟议的多功能融合方法显著提高了虹膜识别的准确性和稳定性.
- 整合质量评估,降噪和高级功能提取可以提高系统性能.
- 这种方法为信息时代提供了更安全,更可靠的身份验证解决方案.
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