Abstract
Face recognition models have made substantial progress due to advances in deep learning and the availability of large-scale datasets. However, reliance on massive annotated datasets introduces challenges related to training computational cost and data storage, as well as potential privacy concerns regarding managing large face datasets. This paper presents DiffProb, the first data pruning approach for the application of face recognition. DiffProb assesses the prediction probabilities of training samples within each identity and prunes the ones with identical or close prediction probability values, as they are likely reinforcing the same decision boundaries, and thus contribute minimally with new information. We further enhance this process with an auxiliary cleaning mechanism to eliminate mislabeled and label-flipped samples, boosting data quality with minimal loss. Extensive experiments on CASIA-WebFace with different pruning ratios and multiple benchmarks, including LFW, CFP-FP, and IJB-C, demonstrate that DiffProb can prune up to 50% of the dataset while maintaining or even, in some settings, improving the verification accuracies. Additionally, we demonstrate DiffProb's robustness across different architectures and loss functions. Our method significantly reduces training cost and data volume, enabling efficient face recognition training and reducing the reliance on massive datasets and their demanding management.
Abstract (translated)
面部识别模型由于深度学习的进步和大规模数据集的可用性而取得了显著进展。然而，依赖于大量标注数据集引入了训练计算成本高、存储需求大以及处理大规模人脸数据集时可能出现的数据隐私问题等挑战。本文介绍了DiffProb，这是第一个面向人脸识别应用的数据精简方法。DiffProb评估每个身份内训练样本的预测概率，并修剪掉那些具有相同或接近预测概率值的样本，因为这些样本很可能是重复强化相同的决策边界，因此贡献的新信息量很小。此外，我们还通过辅助清理机制来消除错误标注和标签翻转的样本，从而在几乎不损失数据的情况下提高数据质量。 我们在CASIA-WebFace上进行了广泛的实验，涵盖了不同的精简比率以及包括LFW、CFP-FP和IJB-C在内的多个基准测试。结果显示，DiffProb可以在保持或甚至在某些情况下提升验证准确率的同时最多修剪掉50%的数据集。此外，我们还展示了DiffProb在不同架构和损失函数下具有良好的鲁棒性。 我们的方法显著降低了训练成本和数据量，使高效的人脸识别训练成为可能，并减少了对大规模数据集及其复杂管理的依赖。
URL
https://arxiv.org/abs/2505.15272
