Image segmentation for the extraction of face using Haar like feature

Abstract EN

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 comparingc 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 [19] and combining Haar feature and skin colour based classifiers.