Building a face detector using Haar cascades

As we discussed earlier, face detection is the process of determining the location of the face in the input image. We will use Haar cascades for face detection. This works by extracting a large number of simple features from the image at multiple scales. The simple features are basically edge, line, and rectangle features that are very easy to compute. It is then trained by creating a cascade of simple classifiers. The Adaptive Boosting technique is used to make this process robust. You can learn more about it at http://docs.opencv.org/3.1.0/d7/d8b/tutorial_py_face_detection.html#gsc.tab=0. Let's take a look at how to determine the location of a face in the video frames captured from the webcam.

How to do it…

1. Create a new Python file, and import the following packages:

   import cv2
   import numpy as np 

2. Load the face detector cascade file. This is a trained model that we can use as a detector:

   # Load the face cascade file
   face_cascade = cv2.CascadeClassifier...