Showing 4 open source projects for "performance"

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  • 1

    OpenFace

    A state-of-the-art facial behavior analysis toolkit

    ...OpenFace is able to deliver state-of-the-art results in all of these mentioned tasks. OpenFace is available for Windows, Ubuntu and macOS installations. It is capable of real-time performance and does not need to run on any specialist hardware, a simple webcam will suffice.
    Downloads: 30 This Week
    Last Update:
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  • 2
    mctc4bmi

    mctc4bmi

    Matrix and Tensor Completion for Background Model Initialization

    MCTC4BMI (Multimodal Compressed Sensing and Tensor Decomposition for Brain-Machine Interfaces) is a MATLAB toolbox designed to process and analyze EEG data. It applies compressed sensing and tensor decomposition techniques to improve brain-machine interface (BMI) performance.
    Downloads: 0 This Week
    Last Update:
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  • 3

    Fingerprint Recognition System

    Fingerprint Recognition System 5.3 - Matlab source code

    ...We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information. Index Terms: Biometrics, FingerCode, fingerprints, flow pattern, Gabor filters, matching, texture, verification.
    Downloads: 0 This Week
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  • 4
    Kernel Adaptive Filtering Toolbox

    Kernel Adaptive Filtering Toolbox

    a Matlab benchmarking toolbox for kernel adaptive filtering

    ...They are suitable for nonlinear filtering, prediction, tracking and nonlinear regression in general. This toolbox includes algorithms, demos, and tools to compare their performance. See the included README file for a list of included algorithms and more details. If you use this toolbox in your research please cite: @inproceedings{vanvaerenbergh2013comparative, author = {Van Vaerenbergh, Steven and Santamar{\'i}a, Ignacio}, booktitle = {2013 IEEE Digital Signal Processing (DSP) Workshop and IEEE Signal Processing Education (SPE)}, title = {A Comparative Study of Kernel Adaptive Filtering Algorithms}, year = {2013}, note = {Software available at \url{https://github.com/steven2358/kafbox/}} }
    Downloads: 0 This Week
    Last Update:
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