Face Recognition In Children: A Longitudinal Study
This addresses the lack of longitudinal datasets for children's face recognition, which is important for applications like security or identification, but is incremental as it builds on existing methods with new data.
The study tackled the problem of face recognition in children by introducing the Young Face Aging (YFA) dataset and analyzing performance over short age-gaps, finding that a quality-aware matcher achieved 98.3% and 94.9% TAR at 0.1% FAR over 6 and 36 months, respectively.
The lack of high fidelity and publicly available longitudinal children face datasets is one of the main limiting factors in the development of face recognition systems for children. In this work, we introduce the Young Face Aging (YFA) dataset for analyzing the performance of face recognition systems over short age-gaps in children. We expand previous work by comparing YFA with several publicly available cross-age adult datasets to quantify the effects of short age-gap in adults and children. Our analysis confirms a statistically significant and matcher independent decaying relationship between the match scores of ArcFace-Focal, MagFace, and Facenet matchers and the age-gap between the gallery and probe images in children, even at the short age-gap of 6 months. However, our result indicates that the low verification performance reported in previous work might be due to the intra-class structure of the matcher and the lower quality of the samples. Our experiment using YFA and a state-of-the-art, quality-aware face matcher (MagFace) indicates 98.3% and 94.9% TAR at 0.1% FAR over 6 and 36 Months age-gaps, respectively, suggesting that face recognition may be feasible for children for age-gaps of up to three years.