Unidata Launches Kids and Teens Aging Datasets
The Dubai-based data company released facial image collections to help developers study how faces change between ages 7 and 21.
Updated on Nov. 10, 2026 in Pre-Teens
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Unidata, a Dubai-based data collection and labeling company, has launched Kids & Teens Aging Datasets for facial age estimation. The collections contain more than 26,000 images from over 2,500 participants aged 7 to 21 across six demographic categories.
Why it matters
A UK study found a lack of high-quality, representative datasets is a barrier to improving facial age estimation for children, and existing systems struggle to separate users within narrow age bands such as under 13 or under 18. Synthetic data cannot capture the real changes a face undergoes as it ages.
The datasets include images of single individuals photographed at different ages, along with annotations describing photo type, accessories and whether other people appear in the frame. Performance of systems trained on them has not been reported.
The players
Unidata
A Dubai-based data collection and labeling company that supplies training datasets for artificial intelligence and biometric applications.
The details
The collections gather images from participants across Caucasian, Asian, African, Hispanic, South Asian and Middle Eastern demographic categories. Annotations allow developers to study how facial features change with age, which the company positions as a response to a shortage of suitable biometric training data.
Timeline
November 10, 2026: Unidata launches the Kids & Teens Aging Datasets.
Roadmap
The UK study found that existing facial age estimation systems struggle to separate users within narrow bands such as under 13 or under 18. Unidata's release is one response to that documented shortage of representative child and teen training data.
Parents and educators may encounter facial age estimation tools in age-gated online services as developers train systems on more representative data. The datasets are commercial products and their use in consumer applications has not been described in the source.
The takeaway
Age-verification tools depend heavily on the quality of their training data, so representation across ages and demographics matters for accuracy. Readers can ask services which age-estimation methods they use and how they handle children's images.
Further reading
Explore more about Pre-Teens development and how technology shapes childhood.
Source note: This article includes information reported by Biometric Update.
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