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.

低功耗消费设备（例如门铃摄像头）的强大身份验证是一个重要且独特的挑战。本研究探索了年龄和衰老对面部验证方法性能的影响。使用两个公共年龄数据集——AgeDB和Morph-II作为基线。采用一种逼真的年龄转换方法，利用各种年龄效果对一组高质量的面部图像进行增强。然后通过使用各种指标（包括接收者操作特征曲线和匹配分数分布）来量化这些合成衰老数据对高性能深度学习面部识别模型的影响。实验结果表明，长期年龄效应仍然是最先进的面部验证方法面临的重要挑战。

门铃摄像头还能在您年纪增长时识别您吗