  This paper proposes a novel adaptive algorithm to extract facial feature
points automatically such as eyebrows corners, eyes corners, nostrils, nose
tip, and mouth corners in frontal view faces, which is based on cumulative
histogram approach by varying different threshold values. At first, the method
adopts the Viola-Jones face detector to detect the location of face and also
crops the face region in an image. From the concept of the human face
structure, the six relevant regions such as right eyebrow, left eyebrow, 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 cumulative
histogram 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 algorithm for eyebrows, eyes and mouth filtering
images and contour algorithm for nose filtering image are applied to extract
our desired corner points automatically. The method was tested on a large BioID
frontal face database in different illuminations, expressions and lighting
conditions and the experimental results have achieved average success rates of
95.27%.
