On the Burstiness of Faces in Set

TL;DR

This paper investigates the widespread phenomenon of burstiness in set-based face recognition, demonstrating its negative impact on performance and proposing methods to detect and mitigate burstiness for improved recognition accuracy.

Contribution

The paper introduces three strategies for detecting bursty faces and proposes a quality-aware GMP to enhance face recognition robustness against burstiness effects.

Findings

Burstiness is common in set-based face recognition.

Suppressing burstiness significantly improves recognition performance.

The proposed methods effectively detect and mitigate burstiness in face sets.

Abstract

Burstiness, a phenomenon observed in text and image retrieval, refers to that particular elements appear more times in a set than a statistically independent model assumes. We argue that in the context of set-based face recognition (SFR), burstiness exists widely and degrades the performance in two aspects: Firstly, the bursty faces, where faces with particular attributes %exist frequently in a face set, dominate the training instances and dominate the training face sets and lead to poor generalization ability to unconstrained scenarios. Secondly, the bursty faces %dominating the evaluation sets interfere with the similarity comparison in set verification and identification when evaluation. To detect the bursty faces in a set, we propose three strategies based on Quickshift++, feature self-similarity, and generalized max-pooling (GMP). We apply the burst detection results on training and evaluation stages to enhance the sampling ratios or contributions of the infrequent faces. When evaluation, we additionally propose the quality-aware GMP that enables awareness of the face quality and robustness to the low-quality faces for the original GMP. We give illustrations and extensive experiments on the SFR benchmarks to demonstrate that burstiness is widespread and suppressing burstiness considerably improves the recognition performance.