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多模式生物识别身份验证系统利用最佳训练的集体分类器,使用特征级融合系统
Khushboo Jha1, Aruna Jain1, Sumit Srivastava1
1Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Jharkhand, India.
概括
这项研究引入了用于增强网络安全的多式生物识别系统 (MBS),将面部和语音特征融合在一起,以实现准确,方便的身份验证. 无接触式系统比传统方法提供了更高的安全性.
科学领域:
- 网络安全和生物识别技术
- 人与计算机的交互
- 数字安全数字安全
背景情况:
- 越来越多的网络威胁需要超越密码和单模式生物识别的先进身份验证.
- 安全的数字平台对于金融和电子商务等日常活动至关重要.
- 传统的方法缺乏强度来应对复杂的网络攻击.
研究的目的:
- 为加强网络安全开发一种多模式生物识别系统 (MBS).
- 为了提高身份验证安全性,准确性和用户方便性.
- 解决现有认证方法的局限性.
主要方法:
- 面部 (生理) 和言语 (行为) 特征的特征级融合.
- 综合分类模型结合了Bi-LSTM和DCNN.
- 使用曼塔射线食优化 (MRFO) 算法进行优化.
主要成果:
- 在Voxceleb1上达到98.23%的准确性,在VidTIMIT数据集上达到97.92%.
- 获得的低等错率 (EER) 为3.23%和3.62%.
- 与传统的优化技术和现有的MBS相比,表现出卓越的性能.
结论:
- 拟议的无接触MBS提供了一个强大的,非侵入性的身份验证解决方案.
- 通过标准设备实现无,实时的生物识别数据采集.
- 为银行和电子商务等高网络弹性应用提供了可行的选择.
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