Bachelor Thesis from the year 2017 in the subject Engineering - Computer Engineering, grade: First Class, University of Portsmouth, language: English, abstract: This project presents a fingerprint recognition system using neural network. To establish an objective assessment of the proposed neural network algorithm, fingerprint images from National institute of standards and technology (NIST) database were used. Image processing operations were carried out on the fingerprints prior to extracting the minutiae which are set as input into the network for verification or identification of a person. However, these processes are crucial to the performance of the neural network.Back-propagation neural network algorithm called Scaled Conjugate Gradient is used to train the network. The aim of this project is to implement a faster and reliable fingerprint minutiae matching algorithm and the Matlab experimental results show that the network has achieved an excellent performance in pattern recognition. Furthermore, the overall error rate is very minimal and the network generates 93.2% of accuracy for the fingerprint recognition system.
Fingerprint Recognition System Using Artifical Neural Network
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Bachelor Thesis from the year 2017 in the subject Engineering - Computer Engineering, grade: First Class, University of Portsmouth, language: English, abstract: This project presents a fingerprint recognition system using neural network. To establish an objective assessment of the proposed neural network algorithm, fingerprint images from National institute of standards and technology (NIST) database were used. Image processing operations were carried out on the fingerprints prior to extracting the minutiae which are set as input into the network for verification or identification of a person. However, these processes are crucial to the performance of the neural network.Back-propagation neural network algorithm called Scaled Conjugate Gradient is used to train the network. The aim of this project is to implement a faster and reliable fingerprint minutiae matching algorithm and the Matlab experimental results show that the network has achieved an excellent performance in pattern recognition. Furthermore, the overall error rate is very minimal and the network generates 93.2% of accuracy for the fingerprint recognition system.
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