SISTEM IDENTIFIKASI SIDIK JARI BERBASIS CONVOLUTIONAL NEURAL NETWORK

AULIA, AZMIN and Dwijayanti, Suci (2019) SISTEM IDENTIFIKASI SIDIK JARI BERBASIS CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Fingerprints are unique identifiers which are not the same as others. Hence fingerprints can be used as biometrics, especially for security system. Various studies have been done to develop a biometric system using fingerprints. However, the proposed algorithms were not able to overcome the problems of high error rates and low accuracy segmentation. Therefore, this study focuses on implementing the Convolutional Neural Network (CNN) algorithm to identify fingerprints and improve the accuracy of identification. The research uses a primary data of 1484 which is taken from 106 registered students doing a practice in the Control and Robotics Laboratory, Department of Electrical Engineering, Faculty of Engineering, Sriwijaya University. Training dataset processed in the Convolutional Neural Network consists of the student's right and left thumbs, with the front-side capture position, right side, left side, upper and lower side of the thumb. In this study, the architecture designed of CNN uses convolutional layer, pooling layer, dropout and softmaxloss as classification, with a learning rate of 0.01, batch size 200, and dropout of 0.825. The testing is performed with new fingerprints which are different from the training data. There are three conditions used in the testing process, namely the initial condition, dropout condition and the condition of the batch normalization. The results showed that the system was able to identify fingerprints with an accuracy of 91.89% in initial conditions and 91.52% after added the dropout function. However, when the network is trained using batch normalization conditions, CNN's accuracy in identifying new data drops to 18.21%. This indicates that the batch normalization has not yet been able to generalize the new fingerprint data.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Convolutional Neural Network (CNN), Deep Learning, Image Recognition, Biometric, Fingerprint
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK1 Electrical engineering--Periodicals. Automatic control--Periodicals. Computer science--Periodicals. Information technology--Periodicals. Automatic control. Computer science. Electrical engineering. Information technology.
Divisions: Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User: Mr Azmin Aulia
Date Deposited: 17 Oct 2019 07:40
Last Modified: 17 Oct 2019 08:16
URI: http://repository.unsri.ac.id/id/eprint/11892

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