Image Segmentation for the Extraction of Face using
Haar Like Feature
Hemlata Arunachalam and Mahesh
Motwani
Department of Computer Science and Engineering, Rajiv Gandhi
Technological University, India
Abstract: The segmentation of an
image for the extraction of face is complex task. This paper presents a method
for segmenting image for the extraction of human faces. The method is based on
Haar Like Features (HLF) and it starts with skin colour detection in an input
image. Then skin region is further processed by finding connected components
and holes. Each connected component is tested to extract eye like holes by
finding circularity and area. Each eye like holes is tested by comparing
correlation coefficient to confirm as eyes. If eye like features exist in the
connected component then the rectangular box is drawn to enclose each eyes,
nose and mouth like region based on the distance parameter between two eye like
holes. Then HLF is detected by finding integral image. Based on the comparison
of haar difference and test rules, the final verification of each connected
component as face is done. The detected face is enclosed in rectangle box using
distance parameter of the line between two eyes. The proposed method is tested
on Bao face database and experimental results shows that the method is
effective and achieves better accuracy of face detection and has low error rate
as compared to Viola-Jones [13] and combining Haar feature and skin colour
based classifiers [3].
Keywords: Skin color, connected component, holes, HLF, haar,
integral image.
Received November18, 2013; accepted May 21, 2014