Research

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  • ssl_teaser

    Accurate Video Action Recognition with a Handful of Labeled Examples

    Action recognition in video remains a very popular research topic. However, current approaches based on supervised learning are hampered by the paucity of high-quality training data. Although a great number of videos are now available on the internet, they are not labeled with the accuracy nor at the temporal granularity required to train such methods. We present a complete action recognition system that learns accurate classifiers from as few as three labeled instances of each class.
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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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    Undergraduate

    During my undergraduate career, I was involved in a number of research projects: Outlier Detection, Intrusion Detection, and Nanoparticle Preparation.
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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.