Face Detection which is topic of this video. We are going to talk about What it is, how it works, and how to implement 70+Frames Per Second Face Detection on CPU using the MediaPipe Library…All in under 5 minutes.
First up, What is face detection. Well its quite intuitive just detecting faces right? Well the more formal definition is that Face detection is a technology that enables computers to identify human faces in digital images. It is a type of object-class detection in which the task is to find the locations and sizes of all objects in an image that belong to a given class, in this case it is faces.
So the reason why you’d be interested in using face detection, is as we mentioned earlier is to find out
- How many faces you have in an image,
- The Size of the face, which can also help determine how far the face is from your camera and
- The location of the face in the image. Manipulating objects, both physical and/or virtual, like in the case of the invisibility shield project, is just one of many applications of face detection. The other application is to get people to like and subscribe to this video…haha just kidding.
Face Detection is not to be confused with Facial Recognition which is the process of identifying each individual rather than all faces being the same. We’ll cover recognition in a separate video.
There are a massive amount of ways in which you can implement Face Detection. Before Deep Learning, face detection use to be done using Haar Cascades.
Eventually it evolved using machine learning to do feature extraction using Histogram of Oriented Gradients, also know as HOG used in Dlib Frontal Face detector. Currently using facedetection can be implemented with a Deep Neural Network or DNN using a ResNet-10 Architecture.
In our implementation we will use BlazeFace which is a ultrafast face detection model that is both lightweight and accurate.
In this tutorial we will be using the MediaPipe implementation of BlazeFace to achieve over 70+ Frames per second on a 720P video and on CPU! That’s right! No need for expensive CUDA enabled GPU’s/
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