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Robust Image Analysis of Faces for Genetic Applications

Author(s): Jan Kalina

This paper is devoted to automatic localization of objects (eyes, mouth) in two- dimensional (2D) grey scale images of faces. Motivated by a practical problem in human genetics, the output of the localization of objects in the given database of images is needed for further tasks in the genetic research. Arobust filter is applied on the image to ensure denoising. Templates are used as the main method. The mouth and both eyes are localized jointly using the weighted Pearson product-moment correlation coefficient or its robust analogy based on robust regression methods. In the database with 212 images of faces the method allows to locate the mouth and eyes correctly in 100 % of cases. Also the robust correlation coefficient based on the least weighted squares regression localizes the mouth and both eyes in 100 % of images of the given database. Robustness aspects of the method are examined with respect to rotation, noise, occlusion and asymmetry in the image. The joint localization of the mouth and both eyes produces the method invariant to rotation of any degree. This work is tailor made for the given images with expected usage of the methods in genetic applications.