Internet Scale Facial Recognition
November 10, 2014
Whatever the privacy qualms, facial recognition software is here to stay and only getting better. (Or worse, depending on one’s perspective.) We’ve found a resource that provides a useful review of algorithms and accuracy: “Computer Vision and Image Understanding” (pdf) is an Elsevier-published paper by the University of Central Florida’s Enrique G. Ortiz and Carnegie Mellon’s Brian C. Becker. Not surprisingly, Facebook photos played a part in the team’s research. The paper’s Abstract explains:
“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.”
I’m no software engineer so, to be honest, I only understand about two-thirds of the preceding paragraph. However, I’m advised by someone who does know a smart vector from a hole in the ground that this is a resource folks interested in the field should check out. He also points us here for supplemental information.
Cynthia Murrell, November 10, 2014
Sponsored by ArnoldIT.com, developer of Augmentext
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