深度混合多式生物识别系统基于五个生物特征的特征级深度融合
Mohammad Hassan Safavipour1, Mohammad Ali Doostari2, Hamed Sadjedi1
1Department of Electrical Engineering, Shahed University, Tehran, Iran.
Computational intelligence and neuroscience
|July 20, 2023
概括
这项研究引入了使用面部,虹膜和指纹数据的深度融合的混合多式生物识别系统. 这种新的方法实现了100%的准确性,增强了安全性和针对假冒攻击的稳定性.
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
- 信息安全 信息安全
背景情况:
- 全球对强大信息安全和监管合规性的需求日益增加.
- 单模生物识别系统在准确性和安全性方面的局限性.
- 混合多式生物识别系统通过融合多个特征来提高识别能力.
研究的目的:
- 为混合多式联络生物识别系统提出并比较三种新的特征级深度融合策略.
- 为了提高识别准确性,安全保证和防伪的稳定性.
- 通过有效的特征融合来解决单模系统的局限性.
主要方法:
- 来自三个来源的五种生物识别特征 (面部,虹膜,指纹) 的特征级深度融合.
- 将特征向量映射到重现内核希尔伯特空间 (RKHS) 进行非线性到线性转换.
- 在RKHS中使用缩小维度 (KPCA,KLDA) 和基于四次数的算法 (KQPCA).
- 采用深度学习和完全连接的层面来实现特征空间融合.
主要成果:
- 在6个数据库的实验验证证明了拟议的混合多式联络生物识别系统的有效性.
- 深度融合方法导致了一个安全而强大的多式联运模板.
- 在混合多式联络生物识别系统中实现了100%的准确性,超过了单式联络和其他多式联络系统.
结论:
- 混合多式联络生物识别系统中特征空间的深度融合显著提高了识别准确性和稳定性.
- 提出的方法为信息安全挑战提供了安全有效的解决方案.
- 融合载体的低维度有助于提高系统性能和防伪攻击.
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