Computer Vision Datasets

1,056 People Living_face & Anti-Spoofing Data. The collection scenes include indoor and outdoor scenes. The data includes male and female. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The data includes multiple postures, multiple expressions, and multiple anti-spoofing samples. The data can be used for tasks such as face payment, remote ID authentication, and face unlocking of mobile phone.

23,110 People Multi-race and Multi-pose Face Images Data. This data includes Asian race, Caucasian race, black race, brown race and Indians. Each subject were collected 29 images under different scenes and light conditions. The 29 images include 28 photos (multi light conditions, multiple poses and multiple scenes) + 1 ID photo. This data can be used for face recognition related tasks.

87,871 Images of 106 Facial Landmarks Annotation Data (complicated scenes)，this dataset includes yellow race, black race, white race and Indian people. In order to be more challenging, the data includes multiple scenes, multiple poses, different ages, light conditions and complicated expressions. This data can be used for tasks such as face detection and face recognition.

1,196 People Multi-race and Multi-pose Face Images & Videos Data, 22 images and 40 videos per person. The collection environment includes indoor and outdoor scenes. This data can be used for face recognition and other tasks.

602 People –3,010 Images Multi-Races Human Body Semantic Segmentation Data,The data diversity includes headphones, body, background,and glasses.In terms of annotation, we adpoted segmentation annotations on headphones, body, background and glasses.The data can be used for tasks such as human body segmentation and the behavior detection of Video conference.

100 People - Face Detection & Face 96 Landmarks Annotation Data. The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, infrared cameras were applied. In terms of annotation, each individual consists of 274 to 299 photos, with annotations for detected facial bounding boxes and 96 facial landmarks. The data can be used for tasks such as facial detection, 96 facial landmark recognition.

The 110 People – Human Face Image Data is gathered through camera shot involving 110 native speakers, developed with a proper balance of gender ratio and age group distribution covering major colour tones human race. 2100 pictures per person with glasses, expressions, camera shooting angle, and lighting conditions. All Attributes are annotated, including gender, age, expression, etc. The overall accuracy rate is ≥ 97%.

568 People - Face Detection & Face 106 Landmarks & Human Body Segmentation Annotation Data in Online Conference Scenes. The ethnic groups include East Asians, Caucasians, Blacks, and Browns, with a primary focus on young adults. Various indoor office scenes were captured, including meeting rooms, cafes, libraries, and bedrooms. In terms of annotation, each individual consists of 61 to 64 photos, with annotations for detected facial bounding boxes and 106 facial landmarks, as well as segmentation annotations for the human body. The data can be used for tasks such as facial detection, 106 facial landmark recognition, and human body segmentation.

11,352 Videos- Surveillance Video Data from Overhead View. The data diversity includes multiple scenes, different time periods. The data can be used for tasks such as human detection, human tracking, and human body attribute analysis and other computer vision task based on surveillance video.