Title

Face Recognition For Web-Scale Datasets

Keywords

Large-scale classification; Open-universe face recognition; Sparse representations; Uncontrolled datasets

Abstract

With millions of users and billions of photos, web-scale face recognition is a challenging task that demands speed, accuracy, and scalability. Most current approaches do not address and do not scale well to Internet-sized scenarios such as tagging friends or finding celebrities. Focusing on web-scale face identification, we gather an 800,000 face dataset from the Facebook social network that models real-world situations where specific faces must be recognized and unknown identities rejected. We propose a novel Linearly Approximated Sparse Representation-based Classification (LASRC) algorithm that uses linear regression to perform sample selection for ℓ1- minimization, thus harnessing the speed of least-squares and the robustness of sparse solutions such as SRC. Our efficient LASRC algorithm achieves comparable performance to SRC with a 100-250 times speedup and exhibits similar recall to SVMs with much faster training. Extensive tests demonstrate our proposed approach is competitive on pair-matching verification tasks and outperforms current state-of-the-art algorithms on open-universe identification in uncontrolled, web-scale scenarios. © 2013 Elsevier Inc. All rights reserved.

Publication Date

1-1-2014

Publication Title

Computer Vision and Image Understanding

Volume

118

Number of Pages

153-170

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.cviu.2013.09.004

Socpus ID

84890568852 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84890568852

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