# Trade-off Between Spatial and Angular Resolution in Facial Recognition

arXiv: https://arxiv.org/abs/2402.07263 · HTML: https://mlsanity.com/paper/2402.07263/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [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)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition](https://mlsanity.com/paper/1712.02514/) (2017)
- [Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range](https://mlsanity.com/paper/1611.05008/) (2016)
- [A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition](https://mlsanity.com/paper/1805.10078/) (2018)
- [Learning Texture Transformer Network for Light Field Super-Resolution](https://mlsanity.com/paper/2210.09293/) (2022)

## Built on by (most cited first, 2 of 2)

- [Light Field Spatial Resolution Enhancement Framework](https://mlsanity.com/paper/2405.02787/) (2024)
- [Jointly Learning Spatial, Angular, and Temporal Information for Enhanced Lane Detection](https://mlsanity.com/paper/2405.02792/) (2024)

## Abstract (arXiv)

Ensuring robustness in face recognition systems across various challenging conditions is crucial for their versatility. State-of-the-art methods often incorporate additional information, such as depth, thermal, or angular data, to enhance performance. However, light field-based face recognition approaches that leverage angular information face computational limitations. This paper investigates the fundamental trade-off between spatio-angular resolution in light field representation to achieve improved face recognition performance. By utilizing macro-pixels with varying angular resolutions while maintaining the overall image size, we aim to quantify the impact of angular information at the expense of spatial resolution, while considering computational constraints. Our experimental results demonstrate a notable performance improvement in face recognition systems by increasing the angular resolution, up to a certain extent, at the cost of spatial resolution.