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تمييز قزحية العين باستخدام التحويل الموجي والشبكات الاصطناعية
Author name:
احمد عز الدين عبد الله
Supervisor name:
هديل نصرت عبد الله
General topic:
Electrical, Electronic and Communications Engineering
Specific topic:
Electronic Engineering
Degree:
Master
University:
University of Technology - Electrical Engineering Department - Electronic Engineering Branch
Language:
English
University location:
Baghdad
First pages:
34T459 - p.pdf
Abstract:
التعرف على قزحية العين هي تقنية القياس الحيوي التي تتناول تحديد الهوية على اساس قزحية الانسان. يعتبر تميز القزحية من انواع التكنولوجيا الحيوية الاكثر دقة المتاحة اليوم بالمقارنة مع انواع كثيرة من التقنيات البيومترية المستخدمة مثل : مسح بصمات الاصابع، التعر | Iris Recognition is a Biometric Technology which deals with identification based on the human iris. It is considered to be the most accurate biometric technology available today compared with many kinds of biometric technologies used, like Fingerprint scanning, Face recognition, Voice recognition and Hand geometry scanning because it has some advantages, such as uniqueness, stability and high recognition rate etc., makes iris recognition so accurate.In this proposed system, two database systems are used. The first is CASIA database system (version 1.0)(Chinese Academy of Sciences Institute of Automation). And, the second is Real database system by using real persons (each with many images) for recognition through camera Mobile Type of Galaxy Note3. An approach to get more accuracy of the offline iris recognition is composed of many steps : capturing the iris image, determining the location of the iris boundaries, normalization, preprocessed using median filter to remove noise, using wavelet transform for two types of filter, Haar and Daubechies, in order to extract the features and finally using the matching by artificial feed forward neural network with back propagation algorithm (FFBNN) for training and testing iris image.In CASIA system, the iris recognition rate for Haar filter was 84.2% and for Daubechies filter was 92.8%, while in the real system, iris recognition rate for Haar filter was 90% and for Daubechies filter was 98.7%. This means the Daubechies filter is the best in execution time and mean square error from the Haar filter. Finally, efficiency of this system is logical, because the performance measurement of False Acceptance Rates was reasonable. The results and the experiments were implemented by P4 computer and the software package MATLAB (R2011a).
