This African dataset features 22 facial images per individual, captured over a 10-year span, along with one recent image. It includes rich metadata, making it ideal for age progression, facial recognition, and longitudinal facial analysis research.
Category
Facial Biometric Data
Total volume
10K+ images
Last Updated
July 2025
Number of participants
500+ people
Welcome to the African Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.
This dataset includes over 10,000+ high-quality facial images, organized into individual participant sets, each containing:
To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:
Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:
This dataset is highly valuable for a wide range of AI and computer vision applications:
To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:
This dataset is developed and owned by FutureBeeAI and is available for commercial use.
Facial Recognition
Security & Surveillance
Demographic
African
Countries
Kenya, Malawi ...moreNigeria, Ethiopia, Benin, Somalia, Uganda, and more
Volume
10K+ images
Gender Distribution
M:60, F:40
Age Group
18-70 Years
Environment
Indoor & Outdoor
Format
JPEG & HEIC
Device
Android & iOS
Minimum Resolution
480p
Annotation
NA
Download Sample Dataset
Download a free sample of this dataset to get more clarity about this set! OR get in touch with one of our expert to get hands on experience 📨
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More in African
Selfies and ID card images of African individuals for facial recognition and verification.
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KYC
African facial images showing expressions like happy, sad, angry, surprised, and more.
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African facial images with occlusions like masks, sunglasses, caps, and more.
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