An Automatic Framework for Segmentation and Digital Inpainting of 2D Frontal Face Images

Abstract

Nowadays applications that use face images as input for people identification have been very common. In general, the input image must be preprocessed in order to fit some normalization and quality criteria. In this paper, we propose a computational framework composed of digital image quality computation, segmentation of the damaged regions on face images by statistical decision, morphological operators and image restoration by inpainting techniques. Additionally, in this work we propose a new method for digital inpainting that considers as relevant information for such restoration the neighboring pixel intensities as well as prior information extracted from an image database. To assess the efficiency of the computational framework proposed, we have used 2D face images from public databases. The results show that our inpainting method performs similarly to the traditional ones on quasihomogeneous regions and gives better results when the damaged areas includes complex image patterns. he has been with the Na-tional Laboratory for ScientificComputing, Petropo-lis, Brazil, where he is responsible for academic research projects in the area of Image Segmentation, Data Visualization and Fluid Animation. More recently, he has worked on the application of SVM in Medical Image Analysis and on new ranking methods for principal component and discriminant analyses.