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

TL;DR

This paper introduces a novel deep embedding learning framework for face recognition that combines sample- and set-based supervision, featuring a new Max-Margin Loss that improves verification accuracy on standard benchmarks.

Contribution

The work proposes a new Max-Margin Loss function for deep face embeddings, integrating set-based supervision with support vector machine concepts to enhance recognition performance.

Findings

Max-Margin Loss outperforms previous loss functions in face verification tasks.

The framework effectively combines sample- and set-based supervision strategies.

Experimental results demonstrate improved accuracy on benchmark datasets.

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.