# Balanced Alignment for Face Recognition: A Joint Learning Approach

arXiv: https://arxiv.org/abs/2003.10168 · HTML: https://mlsanity.com/paper/2003.10168/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

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

- [Prototype Memory for Large-scale Face Representation Learning](https://mlsanity.com/paper/2105.02103/) (2021)
- [Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment](https://mlsanity.com/paper/2209.07220/) (2022)

## Abstract (arXiv)

Face alignment is crucial for face recognition and has been widely adopted. However, current practice is too simple and under-explored. There lacks an understanding of how important face alignment is and how it should be performed, for recognition. This work studies these problems and makes two contributions. First, it provides an in-depth and quantitative study of how alignment strength affects recognition accuracy. Our results show that excessive alignment is harmful and an optimal balanced point of alignment is in need. To strike the balance, our second contribution is a novel joint learning approach where alignment learning is controllable with respect to its strength and driven by recognition. Our proposed method is validated by comprehensive experiments on several benchmarks, especially the challenging ones with large pose.