Research Of Iris Recognition Algorithm Based On Wavelet Transform And Singular Value Decomposition

With the rapid development of information technology, the research on information security has become one of important topics. However, traditional identification technology is inherently insecure and cannot meet current requirement that leads to a massive rise in the interest for biometric personal identification. Iris recognition is an emerging biometric technology. For uniqueness, stability, available collection and noninvasiveness of iris, iris recognition is being more and more regarded by people. Compared with face, fingerprint, palm-print, voice, and other biometric technology, iris recognition has higher precision. In recent years, iris recognition has made progress in technology research and application, and has a wide prospect and market.A new iris recognition algorithm based on discrete wavelet transform and singular value decomposition is presented in this thesis. Firstly, a new iris localization algorithm is proposed because of the slow speed of localization and inaccurate localization. For inner boundary, initial located point in pupil is gotten by average gray method according to gray distribution. Boundary points of pupil are fixed by the edge detection template, then C-Means dynamic clustering algorithm is brought up to improve precision in localization. For outer boundary, the method combining rude localization with accurate localization is adopted . In the range of rude localization and in light of characteristic analysis of iris, improved Daugman operator is applied for accurate localization in one dimension space. The localization algorithm not only avoids repeated iteration under precision condition but also greatly fastens the speed of localization.Secondly, a method based on discrete wavelet transform and singular value decomposition is discussed to extract iris's feature. After divided iris into eight small blocks according to its texture distribution, each small block is processed with bior1.5 wavelet transform and 5 strips are extracted.Then singular value decomposition is applied to each strip and the final feature vector is gained.Finally, an improved weighted adaptive criterion of minimum average distance.is raised in feature matching of iris. Each image is chose as testing image and clustering feature vectors in the CASIA iris database is provided for matching by criterion mentioned above.The results of testing experiments indicate that the algorithm is reliable, efficient. The presented iris recognition algorithm is improved both in speed and accuracy, and performs with good recognition rate of 98.36% on the images of CASIA iris database.