人类微表达在多模式社会行为生物识别中的微表达
Zaman Wahid1, A S M Hossain Bari1, Marina Gavrilova1
1Biometric Technologies Laboratory, Department of Computer Science, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一种新的多式社会行为生物识别 (SBB) 系统. 将人类微表情与其他SBB特征集成,可以显著提高在线用户识别准确性和回忆力.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人与计算机的交互
背景情况:
- 社交行为生物识别 (SBB) 对于在线个人识别至关重要.
- 了解独特的社交互动和沟通模式是关键.
- 现有的SBB系统可以通过新的生物识别特征来增强.
研究的目的:
- 引入一个新的多式联运SBB系统.
- 从文本中整合人类微表达式作为一种新的生物识别特征.
- 为了提高在线用户识别性能.
主要方法:
- 开发了一个多式联运SBB系统,结合了人类的微表情.
- 提取了六个SBB特征,包括微表达式,用于全面的用户表示.
- 雇员等级融合使用加权波尔达计数来结合特征得分.
主要成果:
- 提出的方法在推特用户数据集上实现了73.87%的准确性和74%的回忆率.
- 整合人类微表情大大提高了识别性能.
- 性能优于现有的最先进的SBB系统.
结论:
- 人类微表情是SBB的一个有价值的生物识别特征.
- 综合多种特征的多模式SBB系统提供了卓越的性能.
- 拟议的融合方法有效地提高了在线用户识别.
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