Fusion of Face and Ear Biometrics for Robust Child Recognition: Insights into Age-Dependent Recognition Trends

Biometric recognition of children presents unique challenges due to rapid physiological changes that affect the consistency of extracted features over time. This study presents a longitudinal evaluation of a multimodal biometric system combining facial and ear features for child recognition across a three-year span. The evaluation uses collected datasets containing frontal and profile images of 231 children aged 3 to 18, with seven data collection sessions spaced at six-month intervals. Face recognition was performed using MagFace, while ear recognition was based on a pipeline involving Mask R-CNN for segmentation and an ensemble of VGG16 and MobileNet for feature extraction. Independent evaluations showed an increase in Equal Error Rate (EER) for face from 1.20% to 2.55% over a 36 -month interval, and for ear from $5.62 \%$ to $19.00 \%$ over the same period. When the two modalities were fused at the feature level, the system achieved improved stability with EER values ranging from $0.24 \%$ at 6 months to $1.38 \%$ at 36 months. Additional analysis across age groups revealed that children enrolled at younger ages experienced higher error rates, highlighting the importance of age-aware system design. These results demonstrate the effectiveness of face-ear fusion in supporting inclusive and temporally stable biometric systems, particularly for applications in education, healthcare, and identity tracking in humanitarian contexts.