Face Recognition via Centralized Coordinate Learning
Xianbiao Qi, Lei Zhang Xianbiao Qi and Lei Zhang are with the Department of Computing, the Hong Kong Polytechnic University, Hong Kong, China. Contact e-mails:
Abstract
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 perform
中文速览
人脸识别模型训练时，人脸特征向量与分类权重向量会相互影响，但以往方法往往只单独优化其中一个，忽视了两者联合分布对网络收敛和识别效果的影响。针对这一问题，研究者提出了"集中坐标学习"（Centralized Coordinate Learning, CCL）方法：将人脸特征强制中心化到坐标原点，使特征均匀分散在整个特征空间，同时将分类向量约束在超球面上，从而让训练与测试阶段的相似度度量保持一致；此外还引入了自适应角度间隔（Adaptive Angular Margin, AAM）进一步提升特征的判别力。在仅使用46万张图像的小规模CASIA Webface数据集训练的条件下，CCL在LFW、跨年龄LFW、YouTube Face、MegaFace等六个人脸识别基准上均取得了与当时最优方法持平甚至更好的成绩。这项工作的意义在于：以极低的数据成本、简洁的设计思路，同时规范了特征和分类向量的几何分布，为训练更有效的人脸识别模型提供了一种通用且易于复现的新思路。
原文 arXiv:1801.05678;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1801.05678v1