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Previous studies generally agree that face recognition accuracy is higher forolder persons than for younger persons. But most previous studies were beforethe wave of deep learning matchers, and most considered accuracy only in termsof the verification rate for genuine pairs. This paper investigates accuracyfor 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 asverification rates and ROC curves. We find that accuracy is lower for olderpersons and higher for younger persons. In contrast, a pre deep learningmatcher on the same dataset shows the traditional result of higher accuracy forolder persons, although its overall accuracy is much lower than that of thedeep learning matchers. Comparing the impostor and genuine distributions, weconclude that impostor scores have a larger effect than genuine scores incausing lower accuracy for the older age group. We also investigate the effectsof training data across the age groups. Our results show that fine-tuning thedeep CNN models on additional images of older persons actually lowers accuracyfor the older age group. Also, we fine-tune and train from scratch two modelsusing age-balanced training datasets, and these results also show loweraccuracy for older age group. These results argue that the lower accuracy forthe older age group is not due to imbalance in the original training data.

Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No