# Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision

arXiv: https://arxiv.org/abs/1708.00277 · HTML: https://mlsanity.com/paper/1708.00277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Rethinking the Domain Gap in Near-infrared Face Recognition](https://mlsanity.com/paper/2312.00627/) (2023)

## Abstract (arXiv)

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.