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.

翻译：低功耗消费设备（如门铃摄像头）的鲁棒认证是一项重要且独特的挑战。本研究探讨了年龄和衰老对脸部认证方法性能的影响。工作中使用了两个公开的年龄数据集AgeDB和Morph-II作为基准。采用了一种照片级真实的年龄变换方法，用于增强一组高质量人脸图像，并施加各种年龄效果。随后，通过多种指标（包括接收者操作特征曲线和匹配分数分布）量化了这些合成衰老数据对基于深度学习的高性能人脸识别模型的影响。实验结果表明，长期衰老效应对于当前最先进的脸部认证方法仍是一项重大挑战。