Computer Vision Datasets

This dataset contains 87,871 face images with annotations for 106 facial landmarks, includes yellow, black, white and Indian races. 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 models, face recognition systems, and facial landmark detection algorithms in AI and computer vision.

This dataset contains 500,605 individual face images, with each person represented by a single image. The race distribution includes Asian, Black, Caucasian and Brown individuals, the age distribution is ranging from infant to the elderly, the middle-aged and young people are the majorities. Images were collected in both indoor and outdoor environments, capturing diverse facial expressions, postures, and lighting conditions. The data can be used for tasks such as face recognition, biometric authentication, and computer vision model training. 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.

This dataset contains 2,937 people with 200 images per subject, totaling 587,400 images. Each subject was captured under 4 kinds of light conditions, 10 kinds of occlusion cases (including non-occluded case) and 5 kinds of face poses. This data can be applied to computer vision tasks such as occluded face detection, recognition, and pose-invariant face recognition tasks.

This dataset contains 11,113 people with gauze masks, each contributing 7 images, for a total of 77,791 images. The dataset covers multiple mask types, ages, races, light conditions and scenes. This data can be applied to computer vision tasks such as occluded face detection and recognition, masked face recognition and security systems.

This dataset contains 568 individuals, ethnic groups include East Asians, Caucasians, Blacks, and Browns, primarily young adults. Various indoor office scenes were captured, including meeting rooms, cafes, libraries, and bedrooms. In terms of annotation, each individual has 61 to 64 photos with annotations for detected facial bounding boxes, 106 facial landmarks, and segmentation annotations for the human body. This dataset can be used for tasks like face detection, facial landmark recognition, human body segmentation, and computer vision applications in virtual meeting or office environments.

This dataset contains 25,581 face images. The dataset includes Asian, black, Caucasian and brown races. In order to be more challenging, the data includes multiple scenes, multiple poses, different ages, light conditions and complicated expressions. For annotation, 88 facial landmarks and visible and invisible attributes of landmarks were annotated. This data can be used for tasks such as face detection and recognition models, facial landmark detection algorithms.

This dataset contains 4,253 paired human face images, for each pair, one image without makeup and one with makeup are included. This dataset can be used for tasks such as face recognition and makeup style analysis.

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 children to elderly. 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 AI-based infrared facial recognition and biometric authentication. 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.

1,078 People 3D Faces Collection Data. The collection device is Realsense SR300. Each subject was collected once a week, 6 times in total, so the time span is 6 weeks. The number of videos collected for one subject is 16. The dataset can be used for tasks such as 3D face recognition.