Face recognition for web-scale datasets
Abbreviated Journal Title
Comput. Vis. Image Underst.
Open-universe face recognition; Large-scale classification; Uncontrolled; datasets; Sparse representations; SPARSE REPRESENTATION; DATABASE; CLASSIFICATION; ILLUMINATION; ALGORITHMS; MODEL; POSE; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic
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 El-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. (C) 2013 Elsevier Inc. All rights reserved.
Computer Vision and Image Understanding
"Face recognition for web-scale datasets" (2014). Faculty Bibliography 2010s. 5914.