DiffProb: Data Pruning for Face Recognition

TL;DR

DiffProb is a novel data pruning method for face recognition that reduces dataset size and training costs by removing redundant and mislabeled samples, while maintaining or improving accuracy across various benchmarks.

Contribution

This paper introduces DiffProb, the first data pruning approach tailored for face recognition, enhancing data quality and training efficiency with minimal accuracy loss.

Findings

Prunes up to 50% of training data without accuracy loss

Improves training efficiency and reduces data storage needs

Maintains robustness across different architectures and loss functions

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.