Data_Sheet_1_Human Observers and Automated Assessment of Dynamic Emotional Facial Expressions: KDEF-dyn Database Validation.XLSX

Most experimental studies of facial expression processing have used static stimuli (photographs), yet facial expressions in daily life are generally dynamic. In its original photographic format, the Karolinska Directed Emotional Faces (KDEF) has been frequently utilized. In the current study, we validate a dynamic version of this database, the KDEF-dyn. To this end, we applied animation between neutral and emotional expressions (happy, sad, angry, fearful, disgusted, and surprised; 1,033-ms unfolding) to 40 KDEF models, with morphing software. Ninety-six human observers categorized the expressions of the resulting 240 video-clip stimuli, and automated face analysis assessed the evidence for 6 expressions and 20 facial action units (AUs) at 31 intensities. Low-level image properties (luminance, signal-to-noise ratio, etc.) and other purely perceptual factors (e.g., size, unfolding speed) were controlled. Human recognition performance (accuracy, efficiency, and confusions) patterns were consistent with prior research using static and other dynamic expressions. Automated assessment of expressions and AUs was sensitive to intensity manipulations. Significant correlations emerged between human observers’ categorization and automated classification. The KDEF-dyn database aims to provide a balance between experimental control and ecological validity for research on emotional facial expression processing. The stimuli and the validation data are available to the scientific community.

现有绝大多数面部表情加工的实验研究均采用静态刺激物（即静态照片），但日常生活中的面部表情通常具有动态属性。卡罗林斯卡指向性情绪面孔集（Karolinska Directed Emotional Faces, KDEF）作为原始静态照片格式的数据集，已被广泛应用于相关研究。本研究针对该数据集的动态版本——KDEF-dyn，开展了系统性验证工作。为此，我们借助形变软件，对KDEF数据集中40名模特的面孔进行动画处理，使其从中性表情逐步过渡至6种基础情绪表情（快乐、悲伤、愤怒、恐惧、厌恶与惊讶），整个过渡过程时长为1033毫秒。本研究招募96名人类被试，对生成的240段视频片段刺激物的表情进行分类；同时通过自动化面孔分析技术，针对31种强度梯度下的6种情绪表情与20种面部动作单元（Facial Action Units, AUs）开展特征评估。研究过程中，对图像低阶属性（如亮度、信噪比等）及其他纯粹知觉因素（如画面尺寸、表情过渡速度）均进行了严格控制。人类被试的表情识别表现（包括准确率、识别效率与混淆模式）与此前采用静态及其他动态表情刺激的研究结果保持一致。自动化表情与面部动作单元评估结果对强度梯度变化具有显著敏感性。人类被试的表情分类结果与自动化分类结果之间存在显著相关性。KDEF-dyn数据集旨在为情绪面部表情加工相关研究，在实验控制度与生态效度之间取得平衡。本研究生成的刺激物与验证数据均向全球科研社群开放共享。