Editing Digital image processing (section)

===Face detection===
[[File:Face detection process V1.jpg|thumb|Face detection process]]
[[Face detection]] can be implemented with [[mathematical morphology]], the [[discrete cosine transform]] (DCT), and horizontal [[projection (mathematics)|projection]].

'''General method with feature-based method'''

The feature-based method of face detection is using skin tone, edge detection, face shape, and feature of a face (like eyes, mouth, etc.) to achieve face detection. The skin tone, face shape, and all the unique elements that only the human face have can be described as features.

'''Process explanation'''

# Given a batch of face images, first, extract the skin tone range by sampling face images. The skin tone range is just a skin filter.
## [[Structural similarity]] index measure (SSIM) can be applied to compare images in terms of extracting the skin tone.
## Normally, HSV or RGB color spaces are suitable for the skin filter. E.g. HSV mode, the skin tone range is [0,48,50] ~ [20,255,255]
# After filtering images with skin tone, to get the face edge, morphology and DCT are used to remove noise and fill up missing skin areas.
## Opening method or closing method can be used to achieve filling up missing skin.
## DCT is to avoid the object with skin-like tone. Since human faces always have higher texture.
## Sobel operator or other operators can be applied to detect face edge.
# To position human features like eyes, using the projection and find the peak of the histogram of projection help to get the detail feature like mouth, hair, and lip.
## Projection is just projecting the image to see the high frequency which is usually the feature position.