Possibilistic Reasoning based No-Reference Iris Image Quality Assessment

Abstract

Low quality of data is one of the most critical issues for consumers of data distributed by autonomous sources. The outcomes of non-quality of data, or of poor quality, on decision-making are considerable and have disastrous effects. In this paper, we deal with this problem in the context of biometric applications, specifically those based on the iris modality. In fact, the iris image can be affected by different types of distortion which greatly affect its quality and generate different imperfection forms. Thus, the deterioration of the quality of these images increases the false rejection rate and therefore decreases significantly the performances of iris identification systems. In order to reduce the impact of bad quality of iris image, we add an Image Quality Assessment (IQA) phase to the identification system to reject poor quality images and thus improves system performances. In this paper, a new method based on the possibilistic modeling of imperfect information extracted from the iris images is proposed to assess the iris image quality. The aim of the proposed approach, noted Poss-IIQA, is to handles the different imperfection forms resulting from any type of distortions. As a result, the suggested method key advantage over the baseline system and other earlier approaches, which focuses more on distortions than on imperfections.