AUTOMATIC FACIAL FEATURE POINTS DETECTION IN BESPECTACLED FACES

R. , Saravanan and Sridhar, Usha (2007) AUTOMATIC FACIAL FEATURE POINTS DETECTION IN BESPECTACLED FACES. In: 2nd International Conference on Informatics (Informatics 2007), Hilton Petaling Jaya Hotel, Petaling Jaya, Selangor, Malaysia.

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Official URL: http://dspace.fsktm.um.edu.my/handle/1812/339

Abstract

Detection of eye-centers, a set of key facial feature points, in a bespectacled face has been a challenge in automated face recognition systems because glasses and frames can significantly alter the intensity profile in the region around the eyes. The altered profile usually is caused not only by the spectacle frames, but also by reflections from glass surfaces, which are dependent on illumination conditions and the tints in the glass, leading to poor accuracy in feature point detection. The problem of robust feature point detection in the presence of spectacles in combination of pose and illumination variations has not been fully addressed. This paper describes a new algorithm to extract the eye-centers accurately in the presence of spectacles. The algorithm uses image analysis in combination with a robust rule engine derived from elements of geometrical structure of a face. The novelty of the algorithm stems from the use of local adaptive thresholding to find the lip region, and then filtering out infeasible combinations of shape objects in the eye region using the lip as a reference. Further localization of eye-pupil center in the algorithm delivers eye-center locations with high accuracy. The method handles both variations in illumination and in-depth rotations up to 20 degrees. The paper presents results from the application of the algorithm on a corpus of 939 bespectacled faces obtained from many different public databases. The results show that in 96% of the images the algorithm locates eyecenters within 3 pixels of the pupil found with manual effort

Item Type: Conference or Workshop Item (Paper)
Subjects: Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: MS SITI NUR ATIKAH MOHAMAD RUSDI
Date Deposited: 23 Jul 2013 08:17
Last Modified: 23 Jul 2013 08:17
URI: http://repository.um.edu.my/id/eprint/434

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