A longitudinal study of automatic face recognition

With the deployment of automatic face recognition systems for many large-scale applications, it is crucial that we gain a thorough understanding of how facial aging affects the recognition performance, particularly across a large population. Because aging is a complex process involving genetic and environmental factors, some faces “age well” while the appearance of others changes drastically over time. This heterogeneity (inter-subject variability) suggests the need for a subject-specific aging analysis. In this paper, we conduct such an analysis using a longitudinal database of 147,784 operational mug shots of 18,007 repeat criminal offenders, where each subject has at least five face images acquired over a minimum of five years. By fitting multilevel statistical models to genuine similarity scores from two commercial-off-the-shelf (COTS) matchers, we quantify (i) the population average rate of change in genuine scores with respect to the elapsed time between two face images, and (ii) how closely the subject-specific rates of change follow the population average. Longitudinal analysis of the scores from the more accurate COTS matcher shows that despite decreasing genuine scores over time, the average subject can still be correctly verified at a false accept rate (FAR) of 0.01% across all 16 years of elapsed time in our database. We also investigate (i) the effects of several other covariates (gender, race, face quality), and (ii) the probability of true acceptance over time.