Computer Vision Datasets

1,417 People – 3D Living_Face & Anti_Spoofing Data. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing.

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.

5,521 People - Re-ID Data in Surveillance Scenes. The data includes indoor scenes and outdoor scenes. The data includes males and females, and the age distribution is from children to the elderly. The data diversity includes different age groups, different time periods, different shooting angles, different human body orientations and postures, clothing for different seasons. For annotation, the rectangular bounding boxes and 15 attributes of human body were annotated. The data can be used for re-id and other tasks.

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.

4,484 people multi-race – infrared face recognition data. The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The race distribution includes Asian, Black, Caucasian and Brown people. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is DV-DH4,044S305AD. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

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.

9,000 Images of 180 People - Driver Gesture 21 Landmarks Annotation Data. This data diversity includes multiple age periods, multiple time periods, multiple gestures, multiple vehicle types, multiple time periods. For annotation, the vehicle type, gesture type, person nationality, gender, age and gesture 21 landmarks (each landmark includes the attribute of visible and invisible) were annotated. This data can be used for tasks such as driver gesture recognition, gesture landmarks detection and recognition.