Since the introduction of electronic personal documents in recent years the analysis of passport photographs has become an important field of research. Such photographs have to fulfill a set of minimal quality requirements defined by the International Civil Aviation Organization (ICAO). As some of the specified requirements are related to certain image regions only, these regions must be located in advance. In this work an unsupervised segmentation method for color face images is presented. The developed tool is intended to be part of an automatic passport photograph inspection framework. Our focus is on knowledge based image segmentation. Hence we developed a total variation model that allows us to incorporate prior knowledge about typical passport photographs into the segmentation process. Since uniformity of the image background is one quality requirement defined by ICAO, our tool also contains a background classifier, which decides whether the background region is uniform or not. This enables the inspection framework to reject photographs with a non-uniform background at an early stage. We have conducted several experiments on face images from two different datasets in order to evaluate the performance of our algorithm. The obtained results demonstrate that our method is fairly robust and outperforms other methods targeted at the same problem, in particular an expert system and an AdaBoost classifier.
|Translated title of the contribution||Segmentierung von Gesichtsbildern|
|Publication status||Published - 2008|
Fields of Expertise
- Information, Communication & Computing