静态和动态人脸识别中的一致的眼动模式:一个隐藏的马尔科夫模型研究
Rachel J Bennetts1, Natalie Butcher2, Karen Lander3
1Department of Psychology, Brunel University of London, London UB8 3PH, UK.
Brain sciences
|November 27, 2025
概括
面部识别中的眼动模式在静态和动态刺激之间是稳定的. 隐藏的马尔科夫眼动模型 (EMHMMs) 揭示了一致的视觉策略,有助于面部处理研究.
科学领域:
- 认知心理学 认知心理学
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
背景情况:
- 眼睛的运动对于理解面部处理至关重要.
- 隐藏的马尔科夫眼动模型 (EMHMMs) 识别了常见的观看模式.
- 之前的EMHMM研究集中在静态面孔上,揭示了整体和分析风格.
研究的目的:
- 调查EMHMM识别的面部查看模式是否延伸到动态面部.
- 首次将EMHMM应用于动态人脸识别.
- 通过静态和动态面部刺激来比较眼睛运动策略.
主要方法:
- 参与者学习了静态面孔和动态视频.
- 在学习和识别过程中使用EMHMM分析眼动.
- 评估了面部识别的准确性.
主要成果:
- 两种一致的模式出现了:集中在中心和集中在眼睛.
- 模式在静态和动态条件 (16-27%的切换) 中显示稳定性.
- 在静态学习过程中,对眼睛的焦点模式与更好的静态识别相关.
结论:
- 通过EMHMM识别的面部观看模式从静态刺激泛化为动态刺激.
- 这表明,无论刺激类型如何,看脸行为都稳定.
- 在传统方法之外,EMHMM对面部处理提供了有价值的见解.
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