الفهرس 
Chapter One : IntroductionOnly 14 pages are availabe for public view
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Abstract
Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. But, In the real world applications, uni-modal biometric systems often face limitations because of sensitivity to noise, intra class invariability, data quality, and other factors. Improving the performance of individual matchers in the aforementioned situation may not be effective. Multi biometric systems are used to overcome this problem by providing multiple pieces of evidence of the same identity. This system provides effective fusion scheme that combines information provided by the multiple domain experts based on score-level fusion method, thereby increasing the efficiency which is not possible in uni-modal system. In this thesis, we have proposed the development of a finger vein and iris fusion system which utilizes a single Hamming Distance based matcher to provide higher accuracy than the individual uni-modal system. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. Also, we use algorithm named Scale Invariant Feature Transform (SIFT) for iris and finger vein feature extraction used for identification with normalization and enhancement to achive better performance. The idea of using SIFT algorithm is to use the discriminative interest points generated by SIFT descriptor as feature extraction vector. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein and the proposed technique can accomplish satisfactory recognition performance. Finger vein and iris that have Fuzzy and matching score level with accuracy of 99% with FAR and FRR of 0.1% and 2% ,EER =0.028.