To answer this question, the researchers created a unique resource called the Infants and Toddlers Longitudinal Face (ITLF) dataset. It contains 630 facial images of 30 children collected over seven sessions spanning roughly two years. Unlike most existing datasets, ITLF follows the same children over time, capturing how their faces evolve from infancy into early childhood. Images were taken in realistic conditions, often involving uncooperative subjects, natural backgrounds, and varied lighting. While the dataset is relatively small, its strength lies in showing real facial growth patterns rather than idealized studio conditions.
Testing Modern Face Recognition Models
The results show a clear relationship between age and recognition performance. For infants aged 0 to 6 months, verification accuracy was extremely low. Even the best-performing model correctly verified identities only about 30 percent of the time under strict security conditions. This poor performance reflects how unstable infant facial features are and how similar different infants look at that age. Accuracy gradually improved as children grew older. By the time children reached 2.5 to 3 years, verification accuracy rose to around 65 percent. While still far from perfect, this improvement suggests that facial features become more distinct and stable as toddlers mature. The study also found that short time gaps caused less damage to accuracy than long gaps, confirming that facial change in early childhood is rapid and uneven rather than gradual.
Making Face Recognition More Stable Over Time
What This Means for Real-World Use
The study delivers a clear message: face recognition is not reliable for very young infants, especially those under six months old, and should not be used alone for critical identity decisions at that stage. However, it also shows that performance improves with age and can be further strengthened through adaptive learning techniques. These findings are especially relevant for healthcare systems, missing-child investigations, and smart city services that require secure but ethical identification methods. By highlighting both the limits and possibilities of facial biometrics in early childhood, the research lays the groundwork for safer, more age-aware identity systems that respect the biological realities of growing children.
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