# Set-Based Face Recognition Beyond Disentanglement: Burstiness Suppression With Variance Vocabulary

arXiv: https://arxiv.org/abs/2304.06249 · HTML: https://mlsanity.com/paper/2304.06249/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 65 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Disentangling by Factorising](https://mlsanity.com/paper/1802.05983/) (2018)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Isolating Sources of Disentanglement in Variational Autoencoders](https://mlsanity.com/paper/1802.04942/) (2018)
- [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019)
- [Aggregating Deep Convolutional Features for Image Retrieval](https://mlsanity.com/paper/1510.07493/) (2015)
- [Deeply learned face representations are sparse, selective, and robust](https://mlsanity.com/paper/1412.1265/) (2014)
- [Vector-Decomposed Disentanglement for Domain-Invariant Object Detection](https://mlsanity.com/paper/2108.06685/) (2021)
- [Neural Aggregation Network for Video Face Recognition](https://mlsanity.com/paper/1603.05474/) (2016)
- [Learning Disentangled Representation for Robust Person Re-identification](https://mlsanity.com/paper/1910.12003/) (2019)
- [Disentangle Your Dense Object Detector](https://mlsanity.com/paper/2107.02963/) (2021)
- [Quality Aware Network for Set to Set Recognition](https://mlsanity.com/paper/1704.03373/) (2017)
- [Visualizing Deep Similarity Networks](https://mlsanity.com/paper/1901.00536/) (2019)
- [Decorrelated Adversarial Learning for Age-Invariant Face Recognition](https://mlsanity.com/paper/1904.04972/) (2019)
- [Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition](https://mlsanity.com/paper/1809.00338/) (2018)
- [Reconstruction-Based Disentanglement for Pose-invariant Face Recognition](https://mlsanity.com/paper/1702.03041/) (2017)
- [Multicolumn Networks for Face Recognition](https://mlsanity.com/paper/1807.09192/) (2018)
- [Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)](https://mlsanity.com/paper/1902.07327/) (2019)
- [Generalized Max Pooling](https://mlsanity.com/paper/1406.0312/) (2014)
- [Discriminability Distillation in Group Representation Learning](https://mlsanity.com/paper/2008.10850/) (2020)
- [Interferences in match kernels](https://mlsanity.com/paper/1611.08194/) (2016)

## Built on by (most cited first, 1 of 1)

- [FaceCoresetNet: Differentiable Coresets for Face Set Recognition](https://mlsanity.com/paper/2308.14075/) (2023)

## Abstract (arXiv)

Set-based face recognition (SFR) aims to recognize the face sets in the unconstrained scenario, where the appearance of same identity may change dramatically with extreme variances (e.g., illumination, pose, expression). We argue that the two crucial issues in SFR, the face quality and burstiness, are both identity-irrelevant and variance-relevant. The quality and burstiness assessment are interfered with by the entanglement of identity, and the face recognition is interfered with by the entanglement of variance. Thus we propose to separate the identity features with the variance features in a light-weighted set-based disentanglement framework. Beyond disentanglement, the variance features are fully utilized to indicate face quality and burstiness in a set, rather than being discarded after training. To suppress face burstiness in the sets, we propose a vocabulary-based burst suppression (VBS) method which quantizes faces with a reference vocabulary. With interword and intra-word normalization operations on the assignment scores, the face burtisness degrees are appropriately estimated. The extensive illustrations and experiments demonstrate the effect of the disentanglement framework with VBS, which gets new state-of-the-art on the SFR benchmarks. The code will be released at https://github.com/Liubinggunzu/set_burstiness.