Genuine and Fake Facial Emotion Dataset (GFFD-2025)

The Dual-Task Emotion–Authenticity Facial Expression Dataset (GFFD-2025) is a carefully curated collection of facial images created to support research in emotion recognition and authenticity detection. Unlike traditional emotion datasets, it focuses not only on identifying which emotion a person expresses but also on whether the expression is genuine or acted, contributing to studies in artificial intelligence, affective computing, and human–computer interaction. A total of 2,224 raw facial images were initially collected from voluntary participants. After quality assessment and manual verification, a subset was refined and curated for further research. The dataset repository includes approximately 1,900 raw facial images and around 1,500 cropped and augmented images, representing the cleaned and extended version of the original collection. The dataset covers seven primary emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise; each subdivided into two authenticity categories: Genuine and Fake (Acted). Images were captured under controlled indoor conditions to ensure consistent lighting, neutral backgrounds, and stable face positioning. Genuine expressions were elicited via emotional recall or audiovisual stimuli, while fake expressions were intentionally acted. All data collection sessions were supervised by a certified psychologist to ensure ethical compliance and emotional validity. Images were reviewed and labeled following micro-expression research principles, considering subtle cues such as eye involvement, facial symmetry, muscle tension, and temporal dynamics to distinguish genuine from acted expressions. Curated images were standardized to 224×224 pixels for compatibility with common deep learning frameworks. To enhance dataset diversity and model robustness, images underwent preprocessing and augmentation, including rotation (±30°), width and height shifts (0.2), shear (0.15), zoom (0.2), horizontal flipping, random brightness and contrast adjustments, and normalization to the [0,1] range. This dataset offers a practical benchmark for research in emotion recognition, authenticity detection, human behavior analysis, multitask learning, and explainable AI, enabling development of models sensitive to subtle psychological authenticity cues. Data collection and labeling were conducted at Daffodil International University, Dhaka, Bangladesh, under strict ethical guidelines with informed consent from all participants. Sessions were supervised to ensure participant comfort and authenticity. Supervisor: Md. Mizanur Rahman Lecturer, Department of Computer Science and Engineering Daffodil International University, Dhaka, Bangladesh Email: [e-mail] Data Collectors: Sarah Tasnim Diya (Email: [e-mail]) Most. Jannatul Ferdos (Email: [e-mail]) Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.双任务情绪-真实性面部表情数据集（Dual-Task Emotion–Authenticity Facial Expression Dataset, GFFD-2025）是一套经精心遴选汇编的面部图像集合，旨在为情绪识别与真实性检测相关研究提供支撑。与传统情绪数据集不同，其不仅聚焦于识别人体表达的具体情绪类别，同时还关注该表情是真实流露还是刻意伪装，可为人工智能、情感计算（Affective Computing）以及人机交互（Human–Computer Interaction）领域的研究提供助力。 研究团队最初从自愿参与者处采集了共计2224张原始面部图像。经过质量评估与人工核验后，对其中的子集进行了提纯与整理，以供后续研究使用。本数据集仓库包含约1900张原始面部图像，以及约1500张经裁剪与数据增强（Data Augmentation）后的图像，构成了原始采集集合的清洗与扩展版本。 该数据集涵盖7种基础情绪：愤怒（Angry）、厌恶（Disgust）、恐惧（Fear）、快乐（Happy）、中性（Neutral）、悲伤（Sad）与惊讶（Surprise）；每种情绪均细分为真实（Genuine）与伪装（Fake, Acted）两类真实性标签。所有图像均在可控的室内环境下采集，以确保光照一致、背景中性且面部姿态稳定。真实表情通过情绪回忆或视听刺激诱导产生，而伪装表情则由参与者刻意表演获得。所有数据采集环节均由持证心理学家监督，以确保研究符合伦理规范且情绪诱导有效。 图像的标注遵循微表情（Micro-expression）研究的相关原则，会考量眼部参与度、面部对称性、肌肉张力以及时间动态等细微线索，以区分真实表情与伪装表情。经遴选后的图像均被统一调整为224×224像素规格，以适配主流深度学习框架的使用需求。 为提升数据集的多样性与模型的鲁棒性，研究团队对图像进行了预处理与数据增强操作，包括±30°旋转、宽高偏移（幅度0.2）、剪切变换（系数0.15）、缩放（倍率0.2）、水平翻转、随机亮度与对比度调整，以及归一化至[0,1]区间。 本数据集可为情绪识别、真实性检测、人类行为分析、多任务学习以及可解释人工智能（Explainable AI）相关研究提供实用的基准测试集，助力开发能够感知细微心理真实性线索的模型。 本数据集的采集与标注工作在孟加拉国达卡的达芙妮国际大学（Daffodil International University）开展，严格遵循伦理准则，且所有参与者均签署了知情同意书。所有采集环节均受到监督，以保障参与者的舒适度与数据的真实性。 项目负责人： Md. Mizanur Rahman 计算机科学与工程系讲师 达芙妮国际大学，孟加拉国达卡 电子邮箱：[e-mail] 数据采集人员： Sarah Tasnim Diya（电子邮箱：[e-mail]） Most. Jannatul Ferdos（电子邮箱：[e-mail]） 孟加拉国达卡达芙妮国际大学计算机科学与工程系。