The study of Fingerprint Identification

• Main Authors: Hung-Wen Lee, 李泓汶
• Other Authors: Yung Ting
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/79488761478095754964

碩士 === 中原大學 === 機械工程研究所 === 96 === ABSTRACT The objective of this study is to design a real-time Fingerprint Identification system for the industrial process data security control. Users must complete the fingerprint registry steps and acquire authorization of the fingerprint identification system so as to login. There are three primary units in this system. In Fingerprint Input System, RF sensor is used to scan fingers in order to improve the accuracy. In Fingerprint Feature Process System, image is pre-processed by using image smooth, image enhancement, and image binarization so that image quality is enhanced. Through the process of Fingerprint Direction Computation and Core Point Detection, core point is defined. In the process of Feature Extraction including Gabor Filter, Fan-shape, Normalization and Convolution, features are extracted easily. When capturing features, threshold of each fingerprint is designed and saved into the database system. The Fingerprint Identification Process System is designed based on Euclidean distance algorithm to calculate and indentify the features between the new fingerprints and sample templates. If identification is passed, system will automatic carry out user information; on the contrary, it will show error messages and ask users to input again. Propagation Neural Network (BPNN) for data training is used to increase accuracy of the Fingerprint Identification. In this study, the system execution time measured is less than 4.5 seconds, and the False Accept Rate (FAR) is less than 0.003%, and the False Reject Rate (FRR) less than 0.03%. With fast identification speed and high accuracy rate, the developed identification system is suitable for various application purposes in industry. Key words：Fingerprint Identification, Feature Extraction, Back Propagation Neural Network (BPNN).