Web-Scale Face Recognition
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 ‘1-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.
Detailed Project Page: http://face.enriquegortiz.com
Enrique G. Ortiz and Brian C. Becker, Face Recognition for Web-Scale Datasets
ELSEVIER Computer Vision and Image Understanding (CVIU), September, 2013.
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