改进眼动生物识别:通过神经网络研究新功能
Katarzyna Harezlak1, Ewa Pluciennik1
1Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
研究人员开发了用于眼动生物识别的两个神经网络方法,通过长短期记忆 (LSTM) 网络实现96%的准确性,以确保安全访问. 这项研究验证了眼睛运动动态的正在进行的探索,用于识别.
科学领域:
- 生物识别和人机交互的人机交互
- 人工智能和机器学习
背景情况:
- 安全获取资源变得越来越重要.
- 眼动分析提供了一个有前途的生物识别模式.
- 之前的研究强调了眼睛跟踪用于识别的潜力.
研究的目的:
- 开发和评估使用眼动动态的新型生物识别方法.
- 探索神经网络在分析眼睛运动模式的有效性,以验证用户身份.
- 评估两个不同的特征提取和分类方法的性能.
主要方法:
- 开发了利用神经网络的两种方法,用于基于眼动的识别.
- 方法1:来自100个元素时间序列的眼睛运动动态 (速度,加速,冲动等) 的特征向量. 使用长短期内存 (LSTM) 网络.
- 方法2:统计值来自相同的眼动动态,由密集网络处理.
主要成果:
- 基于LSTM的方法使用时间序列特征实现了96%的高精度.
- 采用统计值和密集网络的第二种方法,准确率为76%.
- 从GazeBase数据集中对眼动记录的三年时间内验证了结果.
结论:
- 通过神经网络分析的眼睛运动动态,提供了一个可行的和准确的生物识别解决方案.
- 基于眼动模式的LSTM方法显示了基于眼动模式的用户识别的卓越性能.
- 需要进一步的研究来完善这些方法,以实现可靠和安全的访问控制.
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