Showing 2 open source projects for "iris recognition source code python"

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  • The Receptionist for iPad | the Original Visitor Management System Icon
    The Receptionist for iPad | the Original Visitor Management System

    Easily keep track of visitors and say goodbye to time-wasting interruptions with The Receptionist for iPad

    The Receptionist for iPad is visitor management software that allows users to calm the chaos of the front office. Our digital check-in solution is customizable to your needs; from your company branding, to configurable buttons and drag-and-drop-design badge printing. Effectively manage and track everyone who comes to your workspace and store the information securely in the cloud: no more paper visitor log!
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  • Hightouch is a data and AI platform for marketing and personalization. Icon
    Hightouch is a data and AI platform for marketing and personalization.

    Marketing needs data and AI. Give them Hightouch.

    Find insights, run real-time campaigns, and build AI agents with all your data.
    Learn More
  • 1
    PRML

    PRML

    PRML algorithms implemented in Python

    PRML repository is a respected and well-maintained project that implements the foundational algorithms from the famous textbook Pattern Recognition and Machine Learning by Christopher M. Bishop, providing a practical and accessible Python reference for both students and professionals. Rather than just summarizing concepts, the repository includes working code that demonstrates linear regression and classification, kernel methods, neural networks, graphical models, mixture models with EM...
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  • 2

    ProximityForest

    Efficient Approximate Nearest Neighbors for General Metric Spaces

    A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer...
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