This paper presents a
face detection algorithm capable of detecting face(s) without prior training as
a face classifier. The technique employed in developing the algorithm is based
on skin segmentation and facial feature extraction of the two eyes and mouth.
Skin segmentation was done in the red, green, blue color space. White balance
correction was employed to correct the change in image temperature that occurs
due to change in lighting conditions at the point of acquiring image.
Morphological operations and bounding box were employed to search and extract
face region from the segmented skin region. Facial feature, eyes and mouth,
were extracted for final verification of the sensed face using the Laplacian of
Gaussian filter and the isosceles triangle matching rules. The extracted
features were used to develop the face detection algorithm. The developed
algorithm was evaluated using random images taken under different lighting conditions.
Furthermore, the efficiency of the developed face detection algorithm was
evaluated using a standard face detection image database. The result obtained
shows that the developed face detection algorithm performed satisfactorily well
with 81.37% detection accuracy. Furthermore, the results obtained from the
performance evaluation of the developed face detection for this study has shown
it clearly that accuracy detection of dissimilar faces in images with complex
background is possible and attainable
Face detection; face detection methods; skin segmentation; morphological operations; isosceles triangle matching