Face recognition across time lapse: On learning feature subspaces

Abstract

There is a growing interest in understanding the impact of aging on face recognition performance, as well as designing recognition algorithms that are mostly invariant to temporal changes. While some success has been made on this front, a fundamental questions has yet to be answered: do face recognition systems that compensate for the effects of aging compromise recognition performance for faces that have not undergone any aging? The studies in this paper help confirm that age invariant systems do seem to decrease performance in non-aging scenarios. This is demonstrated by performing training experiments on the largest face aging dataset studied in the literature to date (over 200,000 images from roughly 64,000 subjects). Further experiments conducted in this research help demonstrate the impact of aging on two leading commercial face recognition systems. We also determine the regions of the face that remain the most stable over time.