Biometric Authentication: A Machine Learning Approach

8.4. Face Detection and Eye Localization

Face (and eye) detection can be viewed as a two-category classification problem. Given an image patch, a face detector decides whether the patch contains a human face (class ω₁) (class ω₀). Misclassification happens when the face detector misses a facial image patch (false rejection) or mistakenly raises a flag on a normal, nonfacial image (false acceptance). According to Bayes's decision theory, the decision rule for the two-category classification problem can be designed by the likelihood ratio (see Eq. 7.4.2):

Equation 8.4.1

where T is the threshold and p(x|ω_i) is the likelihood density of class ω