Learning deep discriminative face features by customized weighted constraint

Different convolutional neural networks (CNNs) may learn different levels of discriminative features to represent the raw face data. To enhance the discrimination of deeply learned face features, we propose a customized weighted discriminative loss (CWD loss) to seek a customized constraint for mitigating the large perturbations caused by imbalanced distribution of correctly-classified features and misclassified features. It focuses on mapping the raw data into a feature space such that deeply learned face features can achieve a high discrimination for representation, by restraining the intra-class variations and the inter-class variations, simultaneously. Extensive experiments carried out on several famous face recognition benchmarks, including LFW, YTF, FGLFW and BLUFR, demonstrate that the proposed approach can achieve superior performance over the related approaches. (C) 2018 Elsevier B.V. All rights reserved.