Longitudinal Study of Automatic Face Recognition
The two underlying premises of automatic face recognition are uniqueness and permanence. This paper investigates the permanence property by addressing the following: Does face recognition ability of state-of-the-art systems degrade with elapsed time between enrolled and query face images? If so, what is the rate of decline w.r.t. the elapsed time? While previous studies have reported degradations in accuracy, no formal statistical analysis of large-scale longitudinal data has been conducted. We conduct such an analysis on two mugshot databases, which are the largest facial aging databases studied to date in terms of number of subjects, images per subject, and elapsed times. Mixed-effects regression models are applied to genuine similarity scores from state-of-the-art COTS face matchers to quantify the population-mean rate of change in genuine scores over time, subject-specific variability, and the influence of age, sex, race, and face image quality. Longitudinal analysis shows that despite decreasing genuine scores, 99% of subjects can still be recognized at 0.01% FAR up to approximately 6 years elapsed time, and that age, sex, and race only marginally influence these trends. The methodology presented here should be periodically repeated to determine age-invariant properties of face recognition as state-of-the-art evolves to better address facial aging.