Customizable 3D avatar-based facial expression stimuli may improve user engagement in behavioral biomarker discovery and therapeutic intervention for autism, Alzheimer's disease, facial palsy, and more. However, there is a lack of customizable avatar-based stimuli with Facial Action Coding System (FACS) action unit (AU) labels. Therefore, this study focuses on (1) FACS-labeled, customizable avatar-based expression stimuli for maintaining subjects' engagement, (2) learning-based measurements that quantify subjects' facial responses to such stimuli, and (3) validation of constructs represented by stimulus-measurement pairs. We propose Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) labeled with AUs by a certified FACS expert. To measure subjects' AUs in response to CADyFACE, we propose a novel Beta-guided Correlation and Multi-task Expression learning neural network (BeCoME-Net) for multi-label AU detection. The beta-guided correlation loss encourages feature correlation with AUs while discouraging correlation with subject identities for improved generalization. We train BeCoME-Net for unilateral and bilateral AU detection and compare with state-of-the-art approaches. To assess construct validity of CADyFACE and BeCoME-Net, twenty healthy adult volunteers complete expression recognition and mimicry tasks in an online feasibility study while webcam-based eye-tracking and video are collected. We test validity of multiple constructs, including face preference during recognition and AUs during mimicry.

翻译：定制化三维虚拟形象的面部表情刺激可改善自闭症、阿尔茨海默病、面瘫等疾病的行为生物标志物发现及治疗干预中的用户参与度。然而，当前缺乏基于可定制虚拟形象且包含面部动作编码系统（FACS）动作单元（AU）标签的刺激素材。因此，本研究聚焦于：（1）具有FACS标签的可定制虚拟化表情刺激以维持受试者参与度，（2）基于学习的量化受试者对此类刺激面部反应的测量方法，（3）刺激-测量配对所表征构念的验证。我们提出经认证FACS专家标注AU的定制化动态面部动作编码表达虚拟形象（CADyFACE）。为了测量受试者响应CADyFACE的AU，我们提出新型贝塔引导相关与多任务表情学习神经网络（BeCoME-Net）用于多标签AU检测。贝塔引导相关损失函数促进特征与AU的相关性，同时抑制特征与受试者身份的相关性以提升泛化能力。我们针对单侧和双侧AU检测训练BeCoME-Net，并与现有最优方法进行对比。为评估CADyFACE与BeCoME-Net的构念效度，二十名健康成年志愿者在在线可行性研究中完成表情识别与模仿任务，同时采集基于网络摄像头的眼动追踪与视频数据。我们验证了多项构念效度，包括识别过程中的面部偏好及模仿过程中的AU表现。