# Face Recognition via Centralized Coordinate Learning

arXiv: https://arxiv.org/abs/1801.05678 · HTML: https://mlsanity.com/paper/1801.05678/

## Key facts

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

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

- [DAIL: Dataset-Aware and Invariant Learning for Face Recognition](https://mlsanity.com/paper/2101.05419/) (2021)
- [Prototype Memory for Large-scale Face Representation Learning](https://mlsanity.com/paper/2105.02103/) (2021)
- [Intra-Class Uncertainty Loss Function for Classification](https://mlsanity.com/paper/2104.05298/) (2021)
- [Center Loss Regularization for Continual Learning](https://mlsanity.com/paper/2110.11314/) (2021)
- [L2BN: Enhancing Batch Normalization by Equalizing the L2 Norms of Features](https://mlsanity.com/paper/2207.02625/) (2022)

## Abstract (arXiv)

Owe to the rapid development of deep neural network (DNN) techniques and the emergence of large scale face databases, face recognition has achieved a great success in recent years. During the training process of DNN, the face features and classification vectors to be learned will interact with each other, while the distribution of face features will largely affect the convergence status of network and the face similarity computing in test stage. In this work, we formulate jointly the learning of face features and classification vectors, and propose a simple yet effective centralized coordinate learning (CCL) method, which enforces the features to be dispersedly spanned in the coordinate space while ensuring the classification vectors to lie on a hypersphere. An adaptive angular margin is further proposed to enhance the discrimination capability of face features. Extensive experiments are conducted on six face benchmarks, including those have large age gap and hard negative samples. Trained only on the small-scale CASIA Webface dataset with 460K face images from about 10K subjects, our CCL model demonstrates high effectiveness and generality, showing consistently competitive performance across all the six benchmark databases.