# CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion

arXiv: https://arxiv.org/abs/2307.10237 · HTML: https://mlsanity.com/paper/2307.10237/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 23 of 23)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](https://mlsanity.com/paper/2004.00288/) (2020)
- [AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations](https://mlsanity.com/paper/1905.00292/) (2019)
- [Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset](https://mlsanity.com/paper/2211.01917/) (2022)
- [Mis-classified Vector Guided Softmax Loss for Face Recognition](https://mlsanity.com/paper/1912.00833/) (2019)
- [Neural Aggregation Network for Video Face Recognition](https://mlsanity.com/paper/1603.05474/) (2016)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Cluster and Aggregate: Face Recognition with Large Probe Set](https://mlsanity.com/paper/2210.10864/) (2022)
- [GhostVLAD for set-based face recognition](https://mlsanity.com/paper/1810.09951/) (2018)
- [Template Adaptation for Face Verification and Identification](https://mlsanity.com/paper/1603.03958/) (2016)
- [Multicolumn Networks for Face Recognition](https://mlsanity.com/paper/1807.09192/) (2018)
- [Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition](https://mlsanity.com/paper/1908.01174/) (2019)
- [RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset](https://mlsanity.com/paper/2307.05563/) (2023)
- [Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect](https://mlsanity.com/paper/1911.12512/) (2019)
- [One-to-many face recognition with bilinear CNNs](https://mlsanity.com/paper/1506.01342/) (2015)
- [Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1605.02686/) (2016)

## Built on by (most cited first, 6 of 6)

- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition](https://mlsanity.com/paper/2412.07771/) (2024)
- [HiFi-Portrait: Zero-shot Identity-preserved Portrait Generation with High-fidelity Multi-face Fusion](https://mlsanity.com/paper/2512.14542/) (2025)
- [Template-based Multi-Domain Face Recognition](https://mlsanity.com/paper/2409.09832/) (2024)
- [Quo Vadis RankList-based System in Face Recognition?](https://mlsanity.com/paper/2410.01498/) (2024)
- [Quality-Aware Multimodal Fusion Reveals Implicit Identity in Valence-Arousal Features](https://mlsanity.com/paper/2607.21347/) (2026)

## Abstract (arXiv)

Face recognition from image sets acquired under unregulated and uncontrolled settings, such as at large distances, low resolutions, varying viewpoints, illumination, pose, and atmospheric conditions, is challenging. Face feature aggregation, which involves aggregating a set of N feature representations present in a template into a single global representation, plays a pivotal role in such recognition systems. Existing works in traditional face feature aggregation either utilize metadata or high-dimensional intermediate feature representations to estimate feature quality for aggregation. However, generating high-quality metadata or style information is not feasible for extremely low-resolution faces captured in long-range and high altitude settings. To overcome these limitations, we propose a feature distribution conditioning approach called CoNAN for template aggregation. Specifically, our method aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness. The proposed method produces state-of-the-art results on long-range unconstrained face recognition datasets such as BTS, and DroneSURF, validating the advantages of such an aggregation strategy.