Will your Doorbell Camera still recognize you as you grow old
Wang Yao Muhammad Ali Farooq Joseph Lemley Peter Corcoran
Robust authentication for low-power consumer devices such as doorbell cameras
poses a valuable and unique challenge. This work explores the effect of age and
aging on the performance of facial authentication methods. Two public age
datasets, AgeDB and Morph-II have been used as baselines in this work. A
photo-realistic age transformation method has been employed to augment a set of
high-quality facial images with various age effects. Then the effect of these
synthetic aging data on the high-performance deep-learning-based face
recognition model is quantified by using various metrics including Receiver
Operating Characteristic (ROC) curves and match score distributions.
Experimental results demonstrate that long-term age effects are still a
significant challenge for the state-of-the-art facial authentication method.