仅靠人脸动态就能识别身份，证明表情变化有独特个人特征。
Investigating Identity Signals in Conversational Facial Dynamics via Disentangled Expression Features
- 用3D人脸模型分离表情动态与静态面容，只保留动态参数。
- 在1429人对话数据上识别准确率达61.14%，远超随机水平。
- 提出漂移噪声比衡量分离可靠性，适用于心理或临床评估。
本研究探讨仅通过面部表情的动态变化是否可实现个体识别，而无需依赖静态面容特征。我们利用FLAME 3D可变形模型，显式分离面部形状与表情动态，从对话视频中逐帧提取表达和下颌系数，仅保留动态信息。在包含1,429名说话者自然对话的CANDOR数据集上，采用监督对比学习的Conformer模型实现了1,429分类任务61.14%的准确率，较随机水平高出458倍，表明面部动态蕴含强身份线索。我们引入漂移到噪声比（DNR），量化形状表达分离的可靠性，通过跨会话形状变化与会内变异性的比值评估。DNR与识别性能呈强负相关，证实形状估计不稳会损害动态识别。研究揭示了对话中存在个体特异性面部动态信号，对社会感知与临床评估具有意义。
原文摘要 · Abstract (English)
This work investigates whether individuals can be identified solely through the pure dynamical components of their facial expressions, independent of static facial appearance. We leverage the FLAME 3D morphable model to achieve explicit disentanglement between facial shape and expression dynamics, extracting frame-by-frame parameters from conversational videos while retaining only expression and jaw coefficients. On the CANDOR dataset of 1,429 speakers in naturalistic conversations, our Conformer model with supervised contrastive learning achieves 61.14\%accuracy on 1,429-way classification -- 458 times above chance -- demonstrating that facial dynamics carry strong identity signatures. We introduce a drift-to-noise ratio (DNR) that quantifies the reliability of shape expression separation by measuring across-session shape changes relative to within-session variability. DNR strongly negatively correlates with recognition performance, confirming that unstable shape estimation compromises dynamic identification. Our findings reveal person-specific signatures in conversational facial dynamics, with implications for social perception and clinical assessment.
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