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

Baris Gecer, Vassileios Balntas, Tae-Kyun Kim; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1665-1672

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 inversely proportional to the distance to it. 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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Gecer_2017_ICCV,
author = {Gecer, Baris and Balntas, Vassileios and Kim, Tae-Kyun},
title = {Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-Based Supervision},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}