--- title: "Child & Teen Facial Dataset for Recognition Systems" description: "Children’s faces change faster than biometric models adapt. We collected real facial data across ages 7 to 15 to track that change over time." url: "https://unidata.pro/cases/child-teen-facial-dataset-for-recognition-systems/" date_modified: "2026-05-13T12:30:05+03:00" language: "en-US" --- 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 -------------- ### Ensuring Age and Identity Consistency - 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 -------- ### 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 ### 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 ### 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 | **Stage** | **Input** | **Workflow Scope** | **Main Quality Checks** | |---|---|---|---| | Project Setup | Client platform & task requirements | Integration, task flow design, access configuration | System connectivity / Task logic consistency | | Participant Onboarding | Contributor pool | Recruitment, onboarding, instruction delivery | Participant diversity / Instruction clarity | | Attack Execution | User devices, printed images, replay materials | Print & replay attacks, iterative submissions | Attack variability / Scenario realism | | Behavior Tracking | Attack attempt data | Tracking attempts, repeat participation, outcome logging | Data completeness / Behavioral consistency | | Validation & Analysis | Collected attack data | System scoring review, performance analysis | Result consistency / Attack success evaluation | | Reporting & Iteration | Validated attack datasets | Weekly reporting, feedback loops, system improvement tracking | Trend accuracy / Continuous performance alignment | ## Hero **Industry and Use Case:** Biometric Security **Data:** Continuous attack-based data generation **Project Duration:** Ongoing project ## Main Title Child & Teen Facial Dataset for Recognition Systems ## Description How does a child’s face change between ages 7 and 15, and why does this matter for biometric security? A biometric security startup developing anti-fraud solutions for minors faced a core limitation of facial recognition systems: they perform poorly on children. The issue is structural — a child’s face changes rapidly, while most models are not designed to adapt to this pace. As a result, outdated photos can be used to bypass Face ID, KYC checks, and parental account protections. We created a multinational dataset that captures year-by-year facial changes between ages 7 and 15. This dataset allows recognition systems to reliably identify children and teenagers in real-world scenarios and reduces the risk of fraud based on old images. ## Progress - Results - Quote ### Progress - Steps **List of Steps:** - **Number of days:** Pilot & Setup — **Step Description:** 2 weeks - **Number of days:** Participant Onboarding — **Step Description:** 3 weeks - **Number of days:** Attack Collection & Iteration — **Step Description:** ongoing - **Number of days:** Monitoring & Reporting — **Step Description:** weekly, ongoing ### Results **List of 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 ### Quote **Quote:** Biometric spoofing resilience is built through repeated real-world attack attempts, not static datasets. System performance improves when diverse participants continuously test its limits under varied conditions. **Author:** Hanna Parkhots **Position:** Data Collection Project Manager [Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)