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How to run deep networks in browser
Introduction
This tutorial will show us how to run deep learning models using OpenCV.js right in a browser. Tutorial refers a sample of face detection and face recognition models pipeline.
Face detection
Face detection network gets BGR image as input and produces set of bounding boxes that might contain faces. All that we need is just select the boxes with a strong confidence.
Face recognition
Network is called OpenFace (project https://github.com/cmusatyalab/openface).
Face recognition model receives RGB face image of size 96x96. Then it returns
128-dimensional unit vector that represents input face as a point on the unit
multidimensional sphere. So difference between two faces is an angle between two
output vectors.
Sample
All the sample is an HTML page that has JavaScript code to use OpenCV.js functionality.
You may see an insertion of this page below. Press Start button to begin a demo.
Press Add a person to name a person that is recognized as an unknown one.
Next we'll discuss main parts of the code.
@htmlinclude js_face_recognition.html
-# Run face detection network to detect faces on input image. @snippet dnn/js_face_recognition.html Run face detection model You may play with input blob sizes to balance detection quality and efficiency. The bigger input blob the smaller faces may be detected.
-# Run face recognition network to receive 128-dimensional unit feature vector by input face image.
@snippet dnn/js_face_recognition.html Get 128 floating points feature vector
-# Perform a recognition. @snippet dnn/js_face_recognition.html Recognize Match a new feature vector with registered ones. Return a name of the best matched person.
-# The main loop. @snippet dnn/js_face_recognition.html Define frames processing A main loop of our application receives a frames from a camera and makes a recognition of an every detected face on the frame. We start this function ones when OpenCV.js was initialized and deep learning models were downloaded.