面向压力发现的自动闪烁检测

Alvaro Marcos-Ramiro, Daniel Pizarro-Perez, Marta Marrón Romera, D. Gática-Pérez
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引用次数: 15

摘要

我们提出了一种鲁棒的方法来自动检测对话视频序列中的眨眼,旨在发现压力。心理学研究表明,眨眼频率和多巴胺水平之间存在关系,而多巴胺水平又受到压力的影响。任务表现与多巴胺和压力水平呈倒U形相关。这表明自动眨眼检测作为一种减少人类编码负担的方法的重要性。我们使用现成的面部追踪器来提取眼部区域。然后,我们对提取的眼睛图像进行逐像素分类,以便随后通过眨眼的动态来识别眨眼。我们用一个带有心理变量注释的求职面试数据库来评估系统的性能,并显示了感知压力抵抗与自动检测到的眨眼模式之间的统计学显著相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Blinking Detection towards Stress Discovery
We present a robust method to automatically detect blinks in video sequences of conversations, aimed to discovering stress. Psychological studies have shown a relationship between blink frequency and dopamine levels, which in turn are affected by stress. Task performance correlates through an inverted U shape to both dopamine and stress levels. This shows the importance of automatic blink detection as a way of reducing human coding burden. We use an off-the-shelf face tracker in order to extract the eye region. Then, we perform per-pixel classification of the extracted eye images to later identify blinks through their dynamics. We evaluate the performance of our system with a job interview database with annotations of psychological variables, and show statistically significant correlation between perceived stress resistance and the automatically detected blink patterns.
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