{"id":60791,"date":"2026-07-13T20:25:48","date_gmt":"2026-07-13T20:25:48","guid":{"rendered":"https:\/\/dmi5324.dotlogicstest.com\/?post_type=case-study&p=60791"},"modified":"2026-07-27T19:43:06","modified_gmt":"2026-07-27T19:43:06","slug":"facial-recognition-ai-video-data-collection","status":"publish","type":"case-study","link":"https:\/\/dmi5324.dotlogicstest.com\/pl\/case-study\/facial-recognition-ai-video-data-collection\/","title":{"rendered":"Creating Real World Video Datasets for Facial Recognition AI"},"content":{"rendered":"\t\t
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The Problem<\/h2>

As facial recognition systems become more widely deployed across security systems and identity verification, AI teams face a significant challenge: ensuring AI models can distinguish between genuine human faces and increasingly sophisticated presentation attacks and spoofing attempts.<\/p>

A global technology company approached DATAmundi to support the creation of a large-scale, highly controlled video dataset designed to improve the performance of its computer vision applications.<\/p>

The goal was to collect authentic video footage that reflected the wide range of real world conditions an AI system might encounter, training the machine to recognize real and spoof scenarios using both synthetic and real identities.<\/p>

The project required far more than simple video capture. Every recording had to follow strict specifications covering lighting conditions, camera distance, viewing angles, accessories, movement, environmental context and metadata. Consistency in these conditions were critical to ensuring the resulting dataset represented realistic attack scenarios and everyday environments.<\/p>

For the real and synthetic identities, the client required a diverse pool of contributors across age, gender and ethnicity, with carefully controlled environmental conditions and consistent data quality across collection sites in Europe and North America.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The Solution<\/h2>

DATAmundi designed and managed an end-to-end data acquisition program, coordinating contributor recruitment, onsite collection, quality assurance and metadata generation.<\/p>

Using its DATAtalent global contributor network, DATAmundi recruited more than 100 participants to create authentic facial video data. Alongside real contributors, hundreds of synthetic identities were prepared. For both real and synthetic identities, masks were created using a variety of materials, enabling the creation of realistic attack scenarios for model training.<\/p>

Contributors were trained using detailed guidelines before attending managed collection sessions in controlled indoor environments. Each contributor participated in a recording session, while additional spoof scenarios using mask-based facial presentations were captured by our trained moderators to ensure consistent, high-quality data across all test conditions. Including:<\/p>