QAMFACE: QUADRATIC ADDITIVE ANGULAR MARGIN LOSS FOR FACE RECOGNITION

The angular-based softmax losses and their variants achieve great success in face recognition based on deep learning. ArcFace which directly maximize decision boundary in angular space is one of the most popular and effective loss function. In this paper, we analyze the inherent limitations of ArcFace, including the non-monotonic logit and gradient curve, and inappropriate trend of loss value. To address these problems, we propose a novel loss function named the Quadratic Additive Angular Margin Loss (QAMFace). It takes the value of the angle through a quadratic function rather than cosine function as the target logit. Our QAMFace is easy to implement and only adds negligible computational overhead. Experiments on several relevant benchmarks show that QAMFace performs better in convergence on feature embedding, and consistently outperforms the state-of-the-art face recognition methods. Our codes will be released soon.