Additive Margin Softmax for Face Verification

作者：Wang, Feng; Cheng, Jian^*; Liu, Weiyang; Liu, Haijun

来源：IEEE Signal Processing Letters, 2018, 25(7): 926-930.

DOI：10.1109/LSP.2018.2822810

• Deep learning
• face verification
• metric learning

In this letter, we propose a conceptually simple and intuitive learning objective function, i.e., additive margin softmax, for face verification. In general, face verification tasks can be viewed as metric learning problems, even though lots of face verification models are trained in classification schemes. It is possible when a large-margin strategy is introduced into the classification model to encourage intraclass variance minimization. As one alternative, angular softmax has been proposed to incorporate the margin. In this letter, we introduce another kind of margin to the softmax loss function, which is more intuitive and interpretable. Experiments on LFW and MegaFace show that our algorithm performs better when the evaluation criteria are designed for very low false alarm rate.

• 出版日期2018-7
• 单位电子科技大学