Creating Multi-View Face Recognition/Detection Database for Deep Learning in Programmatic Way

Let’s talk more technical

Let’s start to think about how we can automatically detect, recognize, crop and save face images from videos. The steps which we will deal;

A scene from Person of Interest TV series.
  • Interactive
  • Interpreted
  • Modular
  • Dynamic
  • Object-oriented
  • Portable
  • High level
  • Extensible in C++ & C

Let’s meet with our face recognizer

Adam Agitey’s face recognizer was developed in Python using OpenFace and dlib. Let’s summarize it quickly;

  1. Encode a picture using the HOG algorithm to create a simplified version of the image. Using this simplified image, find the part of the image that most looks like a generic HOG encoding of a face.
  2. Figure out the pose of the face by finding the main landmarks in the face. Once we find those landmarks, use them to warp the image so that the eyes and mouth are centered.
  3. Pass the centered face image through a neural network that knows how to measure features of the face. Save those 128 measurements.
  4. Looking at all the faces we’ve measured in the past, see which person has the closest measurements to our face’s measurements. That’s our match!
Faces are being tracking, cropping and saving as images from video.
Images are being saving from video with appropriate path hierarchy.
The flow diagram of creating your own face data-set project.



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Ahmet Özlü

Ahmet Özlü


I am a big fan of Real Madrid CF and I love computer science!