With the integration of face recognition technology into important identity applications, it is imperative that the effects of facial aging on face recognition performance are thoroughly understood. As face recognition systems evolve and improve, they should be periodically re-evaluated on large-scale longitudinal face datasets. In our study, we evaluate the performance of two state-of-the-art commercial off the shelf (COTS) face recognition systems on two large-scale longitudinal datasets of mugshots of repeat offenders. The largest of these two datasets has 147,784 images of 18,007 subjects with an average of 8 images per subject over an average time span of 8.5 years. We fit multilevel statistical models to genuine comparison scores (similarity between images of the same face) from the two COTS face matchers. This allows us to analyze the degradation in recognition performance due to elapsed time between a probe (query) and its enrollment (gallery) image. We account for face image quality to obtain a better estimate of trends due to aging, and analyze whether longitudinal trends in genuine scores differ by subject gender and race. Based on the results of our statistical model, we infer that the state-of-the-art COTS matchers can verify 99% of the subjects at a false accept rate (FAR) of 0.01% for up to 10.5 and 8.5 years of elapsed time. Beyond this time lapse of 8.5 years, there is a significant loss in face recognition accuracy. This study extends and confirms the findings of earlier longitudinal studies on face recognition.
Cite this paper as:
D. Deb, L. Best-Rowden, A. K. Jain, "Face Recognition Performance Under Aging", to appear in CVPR, Workshop on Biometrics, 2017.