Face Detection
Explore face detection techniques with OpenCV within image and video processing. Understand how cascades work to detect faces by breaking down images and performing staged tests. Learn to implement face detection in Python, using grayscale conversion and detection parameters to identify multiple faces and draw rectangles around them.
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Face detection with OpenCV
OpenCV uses machine learning algorithms to search for faces within a picture. For something as complicated as a face, there isn’t one simple test that will tell us if it found a face or not. Instead, there are thousands of small patterns and features that must be matched. The algorithms break the task of identifying the face into thousands of smaller, bite-sized tasks that are easy to solve. These tasks are also called classifiers.
For something like a face, we might have 6,000 or more classifiers, all of which must match for a face to be detected. But therein lies the problem, for face detection, the algorithm starts at the top left of a picture and moves down across small blocks of data. It looks at each block, constantly asking, “is this a face?” Since there are 6,000 or more tests per block, at any time there might be millions of calculations running. This would grind any computer to a halt.
The image above is a rough example of how face detection works. The algorithm breaks the image into small blocks of pixels and ...