Object Detection for Face Eye Hand Mouth in Python

This paper describes a face recognition algorithm that extracts the eyes, nostrils and mouth features from cumulative distribution function (CDF) by applying Otsu thresholding. The algorithm, which is inspired by the probability of white pixels of binary facial image, has been tested using the BioID frontal face large database in different illuminations, expressions and lighting conditions. Illumination and lighting variations are addressed using a selective equalization technique. The experimental results have confirmed an average recognition rate of 93.55%.