Haar-Like Features in Face Detection With Python

Key Takeaways

all human faces share some common similarities if you look at a photograph showing a person's face you'll 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 common properties to help us determine if an image contains facial features and ultimately a face a simple way to determine how bright or dark a portion of an images is to first convert it to grayscale and then add up the values of all the pixels within that portion remember lower values represent a darker pixel while higher ones represent a brighter pixel so if a specific sub-regions pixels add up to a low number it's a dark sub-region if it's a high number it's bright 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 har like features 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 har like features 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 horror light features would be able to identify where lines and edges are perfectly but like I said these features are ideal our pictures will never be all black and white that would be too easy instead they're usually varying shades of gray look at this example using Harlech features for finding edges 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 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 this contrast is called the features 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 if we get a result that is close to 255 then we've got a strong contrast and this feature is likely to represent the feature we are looking for it's telling us that whatever it represents like an edge or a line it most likely exists within this region if we get a result closer to zero then the clusters of pixels we are comparing are very close to one another and they're likely isn't any facial feature of interest here something like a wall would be likely to have little contrast as it's all one shade of brightness this example shows how hard light features can be applied to both the eye region and the bridge of the nose it picks up the darkness around the eyes and the bright bridge of the nose because the face has so many distinct areas in terms of brightness horror light features work great here the only problem is this pixel something has to be calculated for many different sub regions of the image at once and that becomes computationally expensive aka slow if only there was a way to speed this up