Dataset Hub

Amid the recent global COVID‑19 outbreak, regions severely affected (e.g., Wuhan) saw near‑universal mask usage, creating a massive pool of samples. We collected these to build the world’s largest masked‑face dataset, released publicly to support current and future public‑safety scenarios. Using this data, we design mask‑occluded face detection and recognition algorithms for closed‑community access control, upgraded facial‑recognition turnstiles at stations and airports, and attendance systems that operate under mask‑cover conditions.

The dataset comprises 647 crowd images, with up to 11,000 individuals, and provides keypoint annotations for precise crowd counting and density estimation. It is designed for crowd counting tasks, especially in highly congested scenes, addressing challenges of varying scales and density. The dataset includes both dense and sparse crowd examples, offering density maps for estimation and supporting crowd monitoring and object detection applications. It enables deep‑learning models to improve crowd control and management, providing rich data such as feature maps and predicted density outputs, and aids public safety monitoring in venues with diverse crowd densities.