In my last tutorial , you learned about convolutional neural networks and the theory behind them. In this tutorial, you’ll learn how to use a convolutional neural network to perform facial recognition using Tensorflow, Dlib, and Docker.
Overview
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Introduction to Facial Recognition
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Preprocessing Images using Facial Detection and Alignment
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Generating Facial Embeddings in Tensorflow
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Training an SVM Classifier
Prerequisites
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Basic understanding of Linear Algebra
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Basic understanding of Convolutional Neural Networks
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Basic Tensorflow understanding
Introduction to Facial Recognition Systems
Facial recognition is a biometric solution that measures unique characteristics about one’s face. Applications available today include flight checkin, tagging friends and family members in photos, and “tailored” advertising.
To perform facial recognition, you’ll need a way to uniquely represent a face. In 1960, Woodrow Bledsoe used a technique involving marking the coordinates of prominent features of a face. Among these features were the location of hairline, eyes and nose.
In 2015, researchers from Google released a paper, FaceNet, which uses a convolutional neural network relying on the image pixels as the features, rather than extracting them manually. It achieved a new record accuracy of 99.63% on the LFW dataset.
FaceNet:
In the FaceNet paper, a convolutional neural network architecture is proposed. For a loss function, FaceNet uses “triplet loss”. Triplet loss relies on minimizing the distance from positive examples, while maximizing the distance from negative examples.
链接：
https://hackernoon.com/building-a-facial-recognition-pipeline-with-deep-learning-in-tensorflow-66e7645015b8
原文链接：
