Face recognition based on adaptive margin and diversity regularization constraints

In recent years, a more robust facial feature can be learned by convolutional neural networks once introducing margins into loss functions. Those methods set a margin for each class manually to squeeze the intra‐class variations within each class equally. However, the internal feature distributions of different persons in the real world are highly unbalanced, and the distance between different identities is not uniform either. As a result, applying the same margin on all classes might not lead to higher inter‐class differences. To address this problem, this paper proposes an adaptive margin based on feature distribution to squeeze the feature interior spaces of different classes. Simultaneously, because the inter‐class margin can adequately represent the distribution of different classes in the feature space, this paper proposes a novel diversity regularization method. The regularization weights of each class are dynamically set depending on their margins. This method proposed in this paper is intuitively interpretable and can be easily applied to other classification scenarios. Experiments on current existing benchmarks have demonstrated the superiority of our method over state‐of‐the‐art competitors.