论文标题
培训数据中的性别平衡如何影响面部识别准确性？
How Does Gender Balance In Training Data Affect Face Recognition Accuracy?
论文作者
论文摘要
深度学习方法大大提高了面部识别的准确性，但是一个旧问题仍然存在：男性的准确性通常高于女性。通常据推测，女性的准确性较低是由于培训数据中代表性不足引起的。这项工作调查了女性在训练数据中的人数不足确实是女性在测试数据上准确性降低的原因。使用最先进的深CNN，三个不同的损失功能和两个培训数据集，我们在七个具有不同男性比率的子集上进行培训，总计四十二个培训，这些培训在三个不同的数据集上进行了测试。结果表明，（1）训练数据中的性别平衡并不能转化为测试准确性中的性别平衡，（2）测试准确性中的“性别差距”不会通过性别平衡的训练集来最小化，而通过具有比女性图像更多的男性图像的训练集，而不是女性图像，并且（3）训练的准确性不会导致最高的女性，男性或平均男性，男性，男性，男性，男性，男性，男性，男性，最高，是最高的，男性的，则是最高的。
Deep learning methods have greatly increased the accuracy of face recognition, but an old problem still persists: accuracy is usually higher for men than women. It is often speculated that lower accuracy for women is caused by under-representation in the training data. This work investigates female under-representation in the training data is truly the cause of lower accuracy for females on test data. 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 trainings, that are tested on three different datasets. Results show that (1) gender balance in the training data does not translate into gender balance in the test accuracy, (2) the "gender gap" in test accuracy is not minimized by a gender-balanced training set, but by a training set with more male images than female images, and (3) training to minimize the accuracy gap does not result in highest female, male or average accuracy
