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Child & Teen Facial Dataset for Recognition Systems

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How does a child’s face change between ages 7 and 15, and why does this matter for biometric security?

A biometric security startup focused on anti-fraud solutions for minors approached us with a critical challenge: facial recognition systems consistently underperform on children. The reason is structural, a child’s face changes faster than most models are trained to adapt. This creates a real attack surface, where outdated photos can be used to bypass Face ID, KYC checks, or parental account protections.

We built a unique multinational dataset, capturing year-by-year facial changes during this critical growth period. The dataset enables recognition systems to accurately identify children and teenagers in real-world scenarios while remaining resilient against fraud attempts using old images.

Industry Biometric Security
Timeline Biometric Data
Data Ongoing project
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Industry Biometric Security
Timeline Biometric Data
Data Ongoing project

Task

The client required a dataset that reflects how facial features evolve throughout childhood and early adolescence. Core requirements included:

  • Accurate age verification for every image
  • Diversity across ethnicity, geography, and gender
  • Year-by-year continuity, allowing models to distinguish natural growth from identity mismatch

Key Challenges

  • 01

    • Verifying real ages without access to official identity documents
    • Covering multiple regions with different cultural and photographic conditions
    • Limited availability of high-quality images of children
    • Ensuring each photo set belonged to the same individual and matched the declared age

Solution

  • 01

    Dataset design and methodology

    • Defined the target age range and prioritized ethnic and regional groups
    • Developed an age-verification approach combining visual assessment and metadata analysis
    • Created clear, standardized instructions for participants and crowd platforms, including capture examples
  • 02

    Data collection

    • Leveraged established crowd platforms and tested new sources to expand geographic coverage
    • Designed simple, engaging tasks to encourage complete and high-quality photo sets
    • Provided fair compensation to reduce drop-off and incomplete submissions
    • Monitored incoming data in real time to address quality issues early
  • 03

    Validation and quality control

    • Combined automated checks with manual expert review to confirm age and photo ownership
    • Applied multi-layer validation, with multiple reviewers cross-checking each submission
    • Minimized inconsistencies and labeling errors, achieving a very low inaccuracy rate
    • Delivered a clean, production-ready dataset suitable for model training and research

Results

  • Achieved high confidence in age accuracy and metadata reliability

  • Enabled training for face recognition, anti-fraud systems, and academic research

  • Identified consistent patterns of facial development across diverse ethnic and regional groups

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