Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision
Abstract
In this work, we investigate several methods and strategies to learn deep embeddings for face recognition, using joint sample- and set-based optimization. We explain our framework that expands traditional learning with set-based supervision together with the strategies used to maintain set characteristics. We, then, briefly review the related set-based loss functions, and subsequently propose a novel Max-Margin Loss which maximizes maximum possible inter-class margin with assistance of Support Vector Machines (SVMs). It implicitly pushes all the samples towards correct side of the margin with a vector perpendicular to the hyperplane and a strength exponentially growing towards to negative side of the hyperplane. We show that the introduced loss outperform the previous sample-based and set-based ones in terms verification of faces on two commonly used benchmarks.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2017
- DOI:
- 10.48550/arXiv.1708.00277
- arXiv:
- arXiv:1708.00277
- Bibcode:
- 2017arXiv170800277G
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- 8 pages, 5 figures, 2 tables, workshop paper