# Mis-classified Vector Guided Softmax Loss for Face Recognition

arXiv: https://arxiv.org/abs/1912.00833 · HTML: https://mlsanity.com/paper/1912.00833/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 30 of 60)

- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC](https://mlsanity.com/paper/2203.15565/) (2022)
- [FaceX-Zoo: A PyTorch Toolbox for Face Recognition](https://mlsanity.com/paper/2101.04407/) (2021)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Consistent Instance False Positive Improves Fairness in Face Recognition](https://mlsanity.com/paper/2106.05519/) (2021)
- [Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain](https://mlsanity.com/paper/2207.07316/) (2022)
- [Face.evoLVe: A High-Performance Face Recognition Library](https://mlsanity.com/paper/2107.08621/) (2021)
- [Controllable and Guided Face Synthesis for Unconstrained Face Recognition](https://mlsanity.com/paper/2207.10180/) (2022)
- [TransFace++: Rethinking the Face Recognition Paradigm with a Focus on Accuracy, Efficiency, and Security](https://mlsanity.com/paper/2308.10133/) (2023)
- [SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition](https://mlsanity.com/paper/2205.12010/) (2022)
- [A Quality Aware Sample-to-Sample Comparison for Face Recognition](https://mlsanity.com/paper/2306.04000/) (2023)
- [Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimation](https://mlsanity.com/paper/2304.10066/) (2023)
- [Physical Adversarial Attacks for Surveillance: A Survey](https://mlsanity.com/paper/2305.01074/) (2023)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [TopoFR: A Closer Look at Topology Alignment on Face Recognition](https://mlsanity.com/paper/2410.10587/) (2024)
- [KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2201.07394/) (2022)
- [Ambiguity-Aware In-Context Learning with Large Language Models](https://mlsanity.com/paper/2309.07900/) (2023)
- [Racial Bias within Face Recognition: A Survey](https://mlsanity.com/paper/2305.00817/) (2023)
- [CoupleFace: Relation Matters for Face Recognition Distillation](https://mlsanity.com/paper/2204.05502/) (2022)
- [Rethinking Impersonation and Dodging Attacks on Face Recognition Systems](https://mlsanity.com/paper/2401.08903/) (2024)
- [CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition](https://mlsanity.com/paper/2304.11668/) (2023)
- [BoundaryFace: A mining framework with noise label self-correction for Face Recognition](https://mlsanity.com/paper/2210.04567/) (2022)
- [CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion](https://mlsanity.com/paper/2307.10237/) (2023)
- [Invariant Feature Regularization for Fair Face Recognition](https://mlsanity.com/paper/2310.14652/) (2023)
- [Locality-aware Channel-wise Dropout for Occluded Face Recognition](https://mlsanity.com/paper/2107.09270/) (2021)
- [Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children](https://mlsanity.com/paper/2110.11630/) (2021)
- [NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition](https://mlsanity.com/paper/2103.03503/) (2021)
- [Low-Mid Adversarial Perturbation against Unauthorized Face Recognition System](https://mlsanity.com/paper/2206.09410/) (2022)
- [Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation](https://mlsanity.com/paper/2411.15555/) (2024)
- [Prototype Memory for Large-scale Face Representation Learning](https://mlsanity.com/paper/2105.02103/) (2021)

## Abstract (arXiv)

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination. To this end, several margin-based (textite.g., angular, additive and additive angular margins) softmax loss functions have been proposed to increase the feature margin between different classes. However, despite great achievements have been made, they mainly suffer from three issues: 1) Obviously, they ignore the importance of informative features mining for discriminative learning; 2) They encourage the feature margin only from the ground truth class, without realizing the discriminability from other non-ground truth classes; 3) The feature margin between different classes is set to be same and fixed, which may not adapt the situations very well. To cope with these issues, this paper develops a novel loss function, which adaptively emphasizes the mis-classified feature vectors to guide the discriminative feature learning. Thus we can address all the above issues and achieve more discriminative face features. To the best of our knowledge, this is the first attempt to inherit the advantages of feature margin and feature mining into a unified loss function. Experimental results on several benchmarks have demonstrated the effectiveness of our method over state-of-the-art alternatives.