On the Normalization of Iris Patterns with Nonlinear Deformation

• Main Authors: Jo-Chin Lu, 呂若堇
• Other Authors: Sheng-Wen Shih
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/69756955224701694054

碩士 === 國立暨南國際大學 === 資訊工程學系 === 99 === Nowadays, automatic iris recognition systems can achieve excellent performance when the pupil sizes of both the enrolled and the test iris images are approximately the same. How- ever, whenthepupilsizechangesdramaticallyduetoeitherilluminationvariationoremotion stimuli, the equal error rate (EER) will increase because of strong nonlinear iris deformation. In this thesis, we focus on the normalization method for compensating the nonlinear iris de- formation effect. Two nonlinear normalization approaches, i.e., the information equalization (IE) method and the dynamic programming (DP) method, are proposed in this study. We also implement Daugman’s rubber sheet model and Yuan’s nonlinear normalization model to be compared with the two proposed methods. Furthermore, a special iris image database called IrisDeform database is constructed with 228 classes of iris patterns from 114 subjects. Each class in the IrisDeform database contains 160 iris patterns acquired at two different sessions with 16 different illumination intensity levels and five iris images for each intensity level. The IrisDeform database is useful for researchers working on the nonlinear normalization problem of iris patterns. Two experiments are conducted to test the nonlinear normalization algorithms. In the first experiment, we enroll the iris images by turns and use the remaining images to method and Yuan’s nonlinear normalization method are 3.21% and 3.51%, respec- tively, whereas the EERs of our IE and DP methods are 4.25% and 2.26%, respectively. The second experiment aims to test the performance of different normalization methods at the extreme cases. The enrolled and the test iris images are acquired at the darkest and the brightest illumination intensity, respectively. The experimental results show that the EERs of both the methods of Daugman and Yuan degraded to 7.21% and 8.05%, respectively, due to strong iris deformation. Conversely, the EER of the DP method only increases to 6.93%. The two experiments show that the proposed DP method is more tolerable to large nonlinear deformations of iris patterns.