Improving Iris Recognition Performance Using Quality Measures

Abstract

Biometric methods, which identify people based on physical or behavioural characteristics, are of interest because people cannot forget or lose their physical characteristics in the way that they can lose passwords or identity cards. Among these biometric methods, iris is currently considered as one of the most reliable biometrics because of its unique texture‘s random variation. Moreover, iris is proved to be well protected from the external environment behind the cornea, relatively easy to acquire and stable all over the person’s life. For all of these reasons, iris patterns become interesting as an alternative approach to reliable visual recognition of persons. This recognition system involves four main modules: iris acquisition, iris segmentation and normalization, feature extraction and encoding and finally matching. However, we noticed that almost all the iris recognition systems proceed without controlling the iris image’s quality. Naturally, poor image’s quality degrades significantly the performance of the recognition system. Thus, an extra module, measuring the quality of the input iris, must be added to ensure that only “good iris” will be treated by the system. The proposed module will be able to detect and discard the faulty images obtained in the segmentation process or which not have enough information to identify person. In literature, most of evaluation quality methods have developed indices to quantify occlusion, focus, contrast, illumination and angular deformation. These measurements are sensitive to segmentation errors. Only few methods have interested on the evaluation of iris segmentation. This chapter aims to present, firstly a novel iris recognition method based on multi-channel Gabor filtering and Uniform Local Binary Patterns (ULBP), then to define a quality evaluation method which integrates additional module to the typical recognition system. Proposed method is tested on Casia v3 iris database. Our experiments illustrate the effectiveness and robustness of ULBP to extract rich local and global information of iris texture when combined with simultaneously multi-blocks and multi-channel method. Also, obtained results show an improvement of iris recognition system by incorporating proposed quality measures in the typical system. This chapter is organized as follows: Section 2 describe in details the proposed iris recognition system. The further represents the quality evaluation method. In section 4, we expose experiments, results and comparison. Finally, the conclusion is given in section 5.