Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision
This work addresses face verification accuracy, offering a method that outperforms previous approaches, though it appears incremental in nature.
The paper tackled face recognition by proposing a novel Max-Margin Loss that combines sample- and set-based supervision to learn deep embeddings, achieving improved verification performance on two benchmarks.
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.