# Minimum Margin Loss for Deep Face Recognition

arXiv: https://arxiv.org/abs/1805.06741 · HTML: https://mlsanity.com/paper/1805.06741/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 5 of 5)

- [Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition](https://mlsanity.com/paper/2102.12570/) (2021)
- [Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System](https://mlsanity.com/paper/2209.04326/) (2022)
- [Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment](https://mlsanity.com/paper/2209.07220/) (2022)
- [Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition](https://mlsanity.com/paper/2401.00719/) (2024)
- [A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures](https://mlsanity.com/paper/2111.12912/) (2021)

## Abstract (arXiv)

Face recognition has achieved great progress owing to the fast development of the deep neural network in the past a few years. As an important part of deep neural networks, a number of the loss functions have been proposed which significantly improve the state-of-the-art methods. In this paper, we proposed a new loss function called Minimum Margin Loss (MML) which aims at enlarging the margin of those overclose class centre pairs so as to enhance the discriminative ability of the deep features. MML supervises the training process together with the Softmax Loss and the Centre Loss, and also makes up the defect of Softmax + Centre Loss. The experimental results on MegaFace, LFW and YTF datasets show that the proposed method achieves the state-of-the-art performance, which demonstrates the effectiveness of the proposed MML.