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Home > All Category Datasets > Computer Vision Datasets > 4,484 People – Multi-Race Infrared Face Recognition Dataset

4,484 People – Multi-Race Infrared Face Recognition Dataset

infrared face recognition dataset
multi-race face dataset
thermal face recognition dataset
AI face recognition training data
multi-age face recognition datase
biometric infrared dataset
facial posture variation dataset
computer vision face recognition dataset
high-diversity face recognition dataset

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.

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SpecificationsSpecifications
Data size
4,484 people, 28 images for each person (RGB + IR)
Population distribution
race distribution: 2,503 Asians, 565 blacks, 683 Caucasians, 733 brown people; gender distribution:2,813 males, 1,671 females; age distribution: ranging from teenager to the elderly, the middle-aged and young people are the majorities
Collecting environment
there were 3,561 people in indoor scenes and 923 people in outdoor scenes
Data diversity
multiple age periods, multiple facial postures, multiple scenes
Device
DV-DH4,044S305AD, the resolution is 1,920*1,080
Data format
the image data format is .jpg, the camera parameter information file format is .txt
Annotation content
label the person – ID, nationality, gender, age, facial action, collecting scene
Accuracy rate
label the person – ID, nationality, gender, age, facial action, collecting scene; the accuracy of label annotation is not less than 97%
Sample Sample
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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.

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