Haar-Like Features

Traditional Face Detection Using Python Austin Cepalia 04:01

All human faces share some similarities. If you look at a photograph showing a person’s face, you will see, for example, that the eye region is darker than the bridge of the nose. The cheeks are also brighter than the eye region. We can use these properties to help us understand if an image contains a human face.

A simple way to find out which region is lighter or darker is to sum up the pixel values of both regions and comparing them. The sum of pixel values in the darker region will be smaller than the sum of pixels in the lighter region. This can be accomplished using Haar-like features.

A Haar-like feature is represented by taking a rectangular part of an image and dividing that rectangle into multiple parts. They are often visualized as black and white adjacent rectangles.

00:00 All human faces share some common similarities. If you look at a photograph showing a person’s face, you will see, for example, that the eye region is darker than the bridge of the nose.

00:14 The cheeks are also brighter than the eye region. We can use these common properties to help us determine if an image contains facial features—and ultimately, a face.

00:25 A simple way to determine how bright or dark a portion of an image is is to first convert it to grayscale, and then add up the values of all the pixels within that portion.

00:38 Remember: lower values represent a darker pixel, while higher ones represent a brighter pixel. So if a specific subregion’s pixels add up to a low number, it’s a dark subregion. If it’s a high number, it’s bright.

00:56 We can determine some features of the images, such as lines and edges, by comparing these pixel readings to readings in an adjacent area. To do this, we use what are called Haar-like features.

01:10 These are ideal clusters of pixels that could represent a specific feature in the image, such as an edge. For example, take a look at these Haar-like features.

01:22 The first two are used to detect edges within a picture, the third one here detects vertical lines, and the fourth one detects horizontal features. If our images were pure black and white, then these Haar-like features would be able to identify where lines and edges are perfectly. But like I said, these features are ideal.

01:45 Our pictures will never be all black and white. That would be too easy. Instead, they’re usually varying shades of gray.

01:54 Take a look at this example using Haar-like features for finding edges.

02:00 The feature on the left represents an edge. That’s because there is a clear contrast between the dark and bright portions of the feature.

02:10 The feature on the right, however, is more realistic. Here, the contrast between the two sides of our potential edge is still pretty distinct, but not as distinct as on the left.

02:24 This contrast is called the feature’s value, and it’s what lets us determine if what the feature represents—like an edge, a line, or a facial feature—exists at this location in the image. To calculate this value, we take the average of the white pixels and subtract from them the average of the black pixels.