Previous studies generally agree that face recognition accuracy is higher for
older persons than for younger persons. But most previous studies were before
the wave of deep learning matchers, and most considered accuracy only in terms
of the verification rate for genuine pairs. This paper investigates accuracy
for age groups 16-29, 30-49 and 50-70, using three modern deep CNN matchers,
and considers differences in the impostor and genuine distributions as well as
verification rates and ROC curves. We find that accuracy is lower for older
persons and higher for younger persons. In contrast, a pre deep learning
matcher on the same dataset shows the traditional result of higher accuracy for
older persons, although its overall accuracy is much lower than that of the
deep learning matchers. Comparing the impostor and genuine distributions, we
conclude that impostor scores have a larger effect than genuine scores in
causing lower accuracy for the older age group. We also investigate the effects
of training data across the age groups. Our results show that fine-tuning the
deep CNN models on additional images of older persons actually lowers accuracy
for the older age group. Also, we fine-tune and train from scratch two models
using age-balanced training datasets, and these results also show lower
accuracy for older age group. These results argue that the lower accuracy for
the older age group is not due to imbalance in the original training data.

该研究使用三个现代的深度卷积神经网络模型，考虑了年龄组 16-29、 30-49 和 50-70 的真实和虚假分布的差异以及验证率和 ROC 曲线。研究结果表明，老年人的识别准确性较低，而年轻人的识别准确性较高，这与传统结果相反，认为老年人的识别准确性更高。研究还探讨了训练数据跨年龄组的影响，结果发现，在原始训练数据不平衡的情况下，增加老年人的图像并不会提高老年人的准确性，并且使用年龄平衡的训练数据集也会导致老年人的准确性降低。