Deep Face Recognition

Omkar M. Parkhi, Andrea Vedaldi and Andrew Zisserman

Abstract

he goal of this paper is face recognition -- from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks.

Session

Poster 1

Files

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PDF iconPaper (PDF, 586K)

DOI

10.5244/C.29.41
https://dx.doi.org/10.5244/C.29.41

Citation

Omkar M. Parkhi, Andrea Vedaldi and Andrew Zisserman. Deep Face Recognition. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 41.1-41.12. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_41,
	title={Deep Face Recognition},
	author={Omkar M. Parkhi and Andrea Vedaldi and Andrew Zisserman},
	year={2015},
	month={September},
	pages={41.1-41.12},
	articleno={41},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.41},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.41}
}