Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 25, Issue 7, Pages 926-930Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2018.2822810
Keywords
Deep learning; face verification; metric learning
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Funding
- National Natural Science Foundation of China [61671125, 61201271, 61301269]
- State Key Laboratory of Synthetical Automation for Process Industries [PAL-N201401]
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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.
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