Face Recognition

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    Facebook Face Recognition

    This work evaluates face recognition applied to the real-world application of Facebook. Because papers usually present results in terms of accuracy on constrained face datasets, it is difficult to assess how they would work on natural data in a real-world application. We present a method to automatically gather and extract face images from Facebook, resulting in over 60,000 faces representing over 500 users. From these natural face datasets, we evaluate a variety of well-known face recognition algorithms (PCA, LDA, ICA, SVMs) against holistic performance metrics of accuracy, speed, memory usage, and storage size.
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    Web-Scale Face Recognition

    In this work, we further analyze the problem of face auto-tagging. With millions of users and billions of photos, web-scale face recognition is a challenging task that demands speed, accuracy, and scalability. 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 SRC methods.
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    Video Face Recognition

    In this work, we employ a large database of still images from the Internet to perform complete video face recognition from face tracking to face track identification. More information coming soon.