Face detection by Haar Classifier

Face detection is a technique that identifies or locates human faces in digital images. Face detection in OpenCV is performed by using classifiers. A classifier is essentially an algorithm that decides whether a given image is positive(face) or negative(not a face). A classifier needs to be trained on thousands of images with and without faces. OpenCV come with a pre-trained Haar feature based face detection classifiers, which can readily be used in a PyFlowOpenCv.

Now let’s create a OpenCV data flow diagram can detection the face in a video or a WebCam.

The result video on videoviewer node will be something like this:

Under the hood, we are using the detectMultiscale module of the OpenCV haar feature classifier. This function will return a rectangle with coordinates(x,y,w,h) around the detected face. This function has two important parameters which have to be tuned according to the data.

• scalefactor: In a group photo, there may be some faces which are near the camera than others. Naturally, such faces would appear more prominent than the ones behind. This factor compensates for that.
• minNeighbors: This parameter specifies the number of neighbors a rectangle should have to be called a face. You can read more about it here.

You can change the two parameters on the fly and see how the parameter changed the detection result.

If you want to detect face from the WebCam, just drag a WebCam node to the diagram instead of the ReadVideo node:

It is also possible to detect face and eye at the same time.