用5秒人脸视频捕捉微表情细节，提升自闭症识别速度与精度
On the Importance of Behavioral Nuances: Amplifying Non-Obvious Motor Noise Under True Empirical Considerations May Lead to Briefer Assays and Faster Classification Processes
- 基于5秒面部视频的微峰信号分析，融合非线性动力学方法
- 在保持个性化统计效力前提下，实现更短时长、更高效率的评估
- 适用于自闭症早期筛查，适合临床快速诊断场景
在获取大规模数据以增强统计效能和构建可扩展、短时采样评估之间存在权衡。传统平均方法常假设生物时间序列数据服从正态分布且为线性平稳过程，导致重要信息被平均掉。本文开发了一种情感计算平台，可在采集极短（5秒）人脸视频的基础上维持个性化统计效力。该方法结合从短暂视频中提取的微峰信号与先进的AI驱动面部网格估计技术，采用几何与非线性动力系统分析法，完整捕获面部微峰，包括不同情绪下的细微表情变化。新方法能有效区分自闭症个体与神经典型发育者之间的动态与几何模式差异。
原文摘要 · Abstract (English)
There is a tradeoff between attaining statistical power with large, difficult to gather data sets, and producing highly scalable assays that register brief data samples. Often, as grand-averaging techniques a priori assume normally-distributed parameters and linear, stationary processes in biorhythmic, time series data, important information is lost, averaged out as gross data. We developed an affective computing platform that enables taking brief data samples while maintaining personalized statistical power. This is achieved by combining a new data type derived from the micropeaks present in time series data registered from brief (5-second-long) face videos with recent advances in AI-driven face-grid estimation methods. By adopting geometric and nonlinear dynamical systems approaches to analyze the kinematics, especially the speed data, the new methods capture all facial micropeaks. These include as well the nuances of different affective micro expressions. We offer new ways to differentiate dynamical and geometric patterns present in autistic individuals from those found more commonly in neurotypical development.
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