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.

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.

This dataset contains 28,972 high-resolution infrared images of 100 individuals in driver scenarios, each annotated with face bounding boxes and 96 facial landmarks. The participants vary in age and race (Caucasian, Black, Indian), and the data reflects realistic driving behaviors including fatigue, distraction, and visual movement. Each subject contributes between 274 and 299 images. Infrared cameras were used to ensure visibility under varying lighting conditions. This dataset is suitable for facial detection, keypoint tracking, driver monitoring system (DMS) development, and other computer vision applications. All data complies with GDPR, CCPA, and PIPL regulations.

This Face Kinship Dataset includes family photos from 4,082 distinct families, with each photo containing at least three family members. A total of 11 types of kinship pairs are represented, such as parent-child, siblings, and grandparent-grandchild relationships. Each image is annotated with facial bounding boxes and the key points of both pupils, enabling accurate analysis of facial relationships. The dataset covers a wide variety of scenes and family structures, making it a valuable resource for AI tasks such as kinship verification, family member recognition, missing person identification, and organizing family photo albums. All data has been collected and processed in compliance with data privacy laws such as GDPR, CCPA, and China’s PIPL.

This large-scale face image dataset features 10,109 individuals from various countries and ethnic backgrounds. Each subject has been captured in multiple real-world scenarios, resulting in diverse facial images under varying angles, lighting conditions, and expressions. Detailed annotations include gender, race, and age, making the dataset suitable for tasks such as facial recognition, face clustering, demographic analysis, and machine learning model training. The dataset has been validated by multiple AI companies and proven to deliver strong performance in real-world applications. All data collection, storage, and processing strictly adhere to global data protection regulations, including GDPR, CCPA, and PIPL, ensuring legal compliance and privacy preservation.

This Human Face Segmentation Dataset contains 70,846 high-quality images featuring diverse subjects with pixel-level annotations. The dataset includes individuals across various age groups—from young children to the elderly—and represents multiple ethnicities, including Asian, Black, and Caucasian. Both males and females are included. The scenes range from indoor to outdoor environments, with pure-color backgrounds also present. Facial expressions vary from neutral to complex, including large-angle head tilts, eye closures, glowers, puckers, open mouths, and more. Each image is precisely annotated on a pixel-by-pixel basis, covering facial regions, five sense organs, body parts, and appendages. This dataset is ideal for applications such as facial recognition, segmentation, and other computer vision tasks involving human face parsing.

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.