  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.
