How important are faces for person re-identification?

TL;DR

This study shows that anonymizing faces in person re-identification datasets by blurring has minimal impact on model performance, enabling privacy-preserving data sharing without sacrificing accuracy.

Contribution

The paper demonstrates that face blurring does not significantly affect re-identification accuracy and that models trained on anonymized data perform comparably to those trained on original data.

Findings

Face blurring has minimal impact on mAP scores.

Training on anonymized data recovers accuracy lost due to face anonymization.

Anonymized datasets can be safely released for research purposes.

Abstract

This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blurring algorithm to create anonymized versions of several popular person re-identification datasets including Market1501, DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing state-of-the-art models that range in accuracy and computational efficiency, we evaluate the effect of this anonymization on re-identification performance using standard metrics. Perhaps surprisingly, the effect on mAP is very small, and accuracy is recovered by simply training on the anonymized versions of the data rather than the original data. These findings are consistent across multiple models and datasets. These results indicate that datasets can be safely anonymized by blurring faces without significantly impacting the performance of person reidentification systems, and may allow for the release of new richer re-identification datasets where previously there were privacy or data protection concerns.