Automatic Adaptive Facial Feature Extraction Using CDF Analysis

Abstract

This paper proposes a novel adaptive algorithm to extract facial feature points automatically such as eyes corners, nostrils, nose tip, and mouth corners in frontal view faces, which is based on histogram representing CDF approach. At first, the method adopts the Viola-Jones face detector to detect the location of face and the four relevant regions such as right eye, left eye, nose, and mouth areas are cropped in a face image. Then the histogram of each cropped relevant region is computed and its CDF value is employed by varying different threshold values to create a new filtering image in an adaptive way. The connected component of interested area for each relevant filtering image is indicated our respective feature region. A simple linear search and a contour algorithm are applied to extract our desired corner points automatically. The method was tested on a large BioID face database and the experimental results have achieved average success rates of 95.56%.