Opensource facial recognition

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Recognize and manipulate faces from Python or from the command line with the world's simplest face recognition library. Built using dlib 's state-of-the-art face recognition built with deep learning.
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Understanding Open-Source Facial Recognition Through OpenFace

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Top 10 Facial Recognition APIs (Updated for ) | RapidAPI

Today smartphones use facial recognition for access control, and animated movies use facial recognition software to bring realistic human movement and expression to life. Police surveillance cameras use it to identify people who have warrants out for their arrest, and it is also being used in retail stores for targeted marketing campaigns. Not all facial recognition libraries are equal in accuracy and performance, and most state-of-the-art systems are proprietary black boxes. OpenFace is an open-source library that rivals the performance and accuracy of proprietary models. FaceNet relies on a triplet loss function to compute the accuracy of the neural net classifying a face and is able to cluster faces because of the resulting measurements on a hypersphere. This results in facial embeddings used for classification for matching or can even be used in a clustering algorithm for similarity detection. During the training portion of the OpenFace pipeline, , images are passed through the neural net.
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Top 10 Facial Recognition APIs of 2019

We have come across some form of facial recognition technology by now. From the authentication access to your professional data in your office to even unlocking your smartphone, facial recognition technology has entered and has become part and parcel of our life now. Now even the governments of many countries are officially adopting it in one form or another for various purposes. Facial recognition technology was first introduced, back in mid 6os.
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Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources. Accuracies from research papers have just begun to surpass human accuracies on some benchmarks. The accuracies of open source face recognition systems lag behind the state-of-the-art.
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