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312 People – 3D Face Recognition & Anti-Spoofing Dataset
face anti spoofing dataset
face liveness detection dataset
3d face recognition dataset
face biometric dataset
This dataset contains facial data from 312 participants collected across diverse indoor and outdoor environments. Participants include males and females ranging from juveniles to older adults, with young and middle-aged participants comprising the majority. Data was collected using iPhone X and iPhone XR devices and covers diverse facial expressions, facial poses, lighting conditions, and real-world environments. This data can be used for tasks such as 3D face recognition, face liveness detection, face anti-spoofing, and facial recognition model training.
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
Specifications
Data size
312 people, 168 images for each person
Population distribution
race distribution: 136 Caucasians, 176 blacks; gender distribution: 191 males, 121 females; age distribution: 301 people aged from 18 to 35, 11 people aged from 36 to 55
What types of computer vision applications can Nexdata’s datasets support?
Nexdata’s computer vision datasets support a wide range of AI applications, including image classification, object detection, image segmentation, facial and human-related recognition, scene understanding, autonomous driving, and other visual perception tasks. Depending on the dataset, data may include images, videos, bounding boxes, polygons, keypoints, segmentation masks, text annotations, and other structured labels.
Can Nexdata customize Computer Vision datasets based on our specific requirements?
Yes. If our off-the-shelf Computer Vision datasets do not fully meet your requirements, Nexdata provides flexible custom data collection, annotation, and curation services. We can customize data based on your target objects, environments, scenarios, camera specifications, geographic locations, data volume, annotation formats, and quality standards to support specific model training and evaluation needs.
How does Nexdata ensure the quality and scalability of its Computer Vision datasets?
Nexdata applies multi-stage quality control throughout data collection, annotation, validation, and delivery. Depending on project requirements, we can implement customized annotation guidelines, multi-level reviews, consistency checks, and quality sampling to ensure dataset accuracy and consistency. Our data collection and processing capabilities can also be scaled to support large-volume Computer Vision projects.
Specifications
Sample
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