Year: 2024
Type: conference-paper
Abstract: The need for reliable identification of children in various emerging applications has sparked interest in leveraging child face recognition technology. This study introduces a longitudinal approach to enrollment and verification accuracy for child face recognition, focusing on the YFA (Young Face Aging) database collected by Clarkson University’s CITeR research group over an 8-year period, at 6-month intervals. The dataset includes children ranging from 3 to 18 years of age, comprising 330 subjects with an average of 6 data collections per subject. Our research aims to comprehensively evaluate the performance of state-of-the- art face-matching techniques on the YFA database, assessing the feasibility of recognizing children's faces upon initial enrollment and verifying their identity longitudinally at 6-month intervals. We conduct a comprehensive analysis of the system’s accuracy considering multiple age groups. We also investigate the temporal degradation of face recognition accuracy over time. Notably, when comparing the initial enrollment image with longitudinal images over an 8-year period, we observe a decrease in accuracy. The average TAR across all age groups is 98.52% with a FAR of 0.1% with a 2-year age verification gap and drops to 95.68 with a 4-year age gap. However, this rate decreases to 87.24% after a time difference of 6 years and further drops to 71.32% with a time difference of 8 years. The highest drop in accuracy was noticed in the age group of (3-5) years old children and the lowest in (5.5-7) years old. By addressing the challenges and opportunities in child face recognition, this research contributes significantly to the advancement of technology for identifying missing or abducted children and other critical applications requiring dependable biometric recognition in children.
Institution: Clarkson University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404238897