The Impact of Age and Threshold Variation on Facial Recognition Algorithm Performance Using Images of Children

Abstract

Facial recognition across ageing and in particular with images of children remains a challenging problem in a wide of range of operational settings. Yet, research examining algorithm performance with images of children is limited with minimal understanding of how age and age variation (i.e., age difference between images being compared) impacts on performance. Operationally, a fixed threshold based on images of adults may be used without considering that this could impact on performance with children. Threshold variation based on age and age variation may be a better approach when comparing images of children. This paper evaluates the performance of a commercial off-the-shelf (COTS) facial recognition algorithm to determine the impact that age (0–17 years) and age variation (0–10 years) has on a controlled operational dataset of facial images using both a fixed threshold and threshold variation approach. This evaluation shows that performance of children differs considerably across age and age variation, and in some operational settings, threshold variation may be beneficial for conducting facial recognition with children.