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Understanding Facial Recognition through Open Face

Today’s world smartphones are using facial recognition for access control while animated movies use it to bring realistic movement and expression to life. Surveillance cameras uses face recognition software to identify citizens. And we’ve used in apps for auto tagger that classifies us, our friends, and our family. It can be used in many different fields of applications, but not all facial recognition libraries are equal in accuracy and performance and most state-of-the-art systems are proprietary black boxes.
OpenFace is a deep learning facial recognition model, it’s widely adopted because it offers high levels of accuracy similar to facial recognition models found in private state-of-the-art systems, OpenFace uses Torch, a scientific computing framework to do training offline, meaning it’s only done once by OpenFace and the user doesn’t have to get their hands dirty training hundreds of thousands of images themselves. Then the images are kept into a neural net for feature extraction using Google’s FaceNet model. It 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.
Major tracks summoned are Artificial Intelligence, Cognitive Computing, Self-Organizing Neural Networks, Backpropagation, Computational Creativity, Artificial Neural Networks, Deep Learning, Ambient Intelligence, Perceptrons, Cloud Computing, Autonomous Robots, Support Vector Machines, Parallel Processing, Bioinformatics, Ubiquitous Computing, Natural Language Processing, and Entrepreneurs Investment Meet.
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