Even though deep learning methods have greatly increased the overall accuracy of face recognition, an old problem still persists: accuracy is higher for men than for women. Previous researchers have speculated that the difference could be due to cosmetics, head pose, or hair covering the face. It is also often speculated that the lower accuracy for women is caused by women being under-represented in the training data. This work aims to investigate if gender imbalance in the training data is actually the cause of lower accuracy for females. Using a state-of-the-art deep CNN, three different loss functions, and two training datasets, we train each on seven subsets with different male/female ratios, totaling forty two train-ings. The trained face matchers are then tested on three different testing datasets. Results show that gender-balancing the dataset has an overall positive effect, with higher accuracy for most of the combinations of loss functions and datasets when a balanced subset is used. However, for the best combination of loss function and dataset, the original training dataset shows better accuracy on 3 out of 4 times. We observe that test accuracy for males is higher when the training data is all male. However, test accuracy for females is not maximized when the training data is all female. Fora number of combinations of loss function and test dataset, accuracy for females is higher when only 75% of the train-ing data is female than when 100% of the training data is female. This suggests that lower accuracy for females is nota simple result of the fraction of female training data. By clustering face features, we show that in general, male faces are closer to other male faces than female faces, and female faces are closer to other female faces than male faces

该研究使用深度卷积神经网络训练数据集，在测试数据集上测试面部识别的准确度，研究发现在训练数据中性别平衡并不会转化为测试准确度的性别平衡，更多的男性图像比女性图像能够更好地缩小测试准确性的“性别差距”，而以缩小准确性差距进行的训练并不会产生最高的男性、女性或平均准确性。

训练数据中的性别平衡如何影响面部识别精度？