Will your Doorbell Camera still recognize you as you grow old

TL;DR

This paper investigates how aging impacts facial recognition accuracy in low-power devices like doorbell cameras, highlighting the challenges faced by current deep-learning models in recognizing aging faces.

Contribution

It introduces a synthetic age transformation method and evaluates its impact on face recognition performance over time, revealing persistent challenges due to aging.

Findings

Long-term aging significantly affects recognition accuracy.

Synthetic aging data can be used to evaluate model robustness.

State-of-the-art methods still struggle with aging effects.

Abstract

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.