Human-Computer Interaction (HCI) has evolved significantly with the integration of facial gesture recognition, offering intuitive and hands-free control mechanisms. This paper presents the Facial Gesture (FaceGest), a comprehensive dataset designed to facilitate research and development in facial gesture recognition systems. The dataset comprises 13 distinct facial gesture classes, including eye-based, mouth-based, head-based, and combined gestures, captured from a diverse group of participants under various lighting conditions, angles, and environments. FaceGest contains approximately 15,000 labeled samples in both video and image formats, providing a robust foundation for training and evaluating machine learning models. Potential applications include hands-free accessibility solutions, automotive systems, smart home automation, AR/VR interactions, security authentication, and gaming controls. By offering this open-access dataset along with baseline models and evaluation metrics, FaceGest aims to bridge existing gaps in HCI datasets and promote the development of inclusive, efficient, and versatile interaction systems.
@InProceedings{--_2025_CVPR,
author = {--, Yaseen and Jamil, Sonain},
title = {FaceGest: A Comprehensive Facial Gesture Dataset for Human-Computer Interaction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops},
month = {June},
year = {2025},
pages = {337-347}
}