The Bash command passes in the image name pic.jpg as a parameter to face-box.py the photo shows yours truly in the hellishly hot desert of Arizona ( Figure 1). The Bash command changes to that directory and calls face-box.py in the container where the face recognition tool is installed.
This binds the current directory (where both the Python script and the test image are located) to the /build directory in the container. To call the script in Listing 1 for face recognition in the container, the user copies it to the examples directory of the GitHub repository's clone and calls docker run -v `pwd`:/build -it face bash -c "cd /build python3 face-box.py pic.jpg" A coffee break would be a good thing while this is going. In the project directory loads everything off the web, compiles dlib, and also loads a 100MB-heavy neural model that is already trained about faces into the container. The docker file in the top directory handles the installation of the dependent projects. This git command creates a clone of the GitHub repository in a local directory. Hosted on GitHub, Adam Geitgey's face_recognition project uses the popular dlib library to recognize faces in photos and videos. Instead, the network is trained with millions of different faces during the learning phase and then recognizes everything that looks remotely similar with a small remaining margin of error. They do not act on fixed pixel values, which wouldn't work, because even two photos of the same person differ enormously at the pixel level.
Neural networks are excellent for this purpose. Assured After TrainingĪnything that could be a face needs to be extracted from the background noise of the image.
Figure 2: The face-box.py script identified a face in the photo and framed it.Ī good face recognition program not only recognizes full-screen faces on portrait photos, but also faces that only cover a few hundred pixels due to the subjects being further away or warped because they are looking into the camera from an angle.
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