ENHANCING IMAGE FINGERPRINT THROUGH WAVELET TRANSFORM METHOD: AN EVALUATION WITH CONVOLUTIONAL NEURAL NETWORKS (CNN)

HAFIQI LAURI, HAFIQI LAURI and Hendy, Santosa and Novalio, Daratha (2023) ENHANCING IMAGE FINGERPRINT THROUGH WAVELET TRANSFORM METHOD: AN EVALUATION WITH CONVOLUTIONAL NEURAL NETWORKS (CNN). Undergraduated thesis, Fakultas Teknik.

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Abstract

The problem of low image quality is even worse with multiple fingerprints because the recognition process depends heavily on the quality of the fingerprint. In general, fingerprints can be divided into seven types. Fingerprint image enhancement is an essential preprocessing technique in fingerprint-based personal authentication systems. This study aims to design a fingerprint image enhancement system using the CNN model to classify fingerprints into seven classes and then analyze the effect of fingerprint image enhancement on CNN performance and classification. Based on the Two Dimension Discrete Wavelet Transform (2D-DWT), Singular Value Decomposition (SVD) and Otsu Threshold, adjustments have been applied to the image processing system, significantly reducing noise and improving image quality because it can control the value contained in each image pixel. The enhanced image is used as a dataset in the classification process, namely the Convolutional Neural Network (CNN). This study's results indicate an increase in the level of accuracy produced by the CNN module from the original image with the enhanced image. The result of comparing the accuracy value between the original image and the enhanced image, the enhanced image has a higher accuracy value compared to the original image. In the FVC2002 fingerprint dataset, the accuracy value of the original image is only 65% to 87% after the enhancement process. In the FVC2004 dataset, the accuracy value of the original image is only 58% to 81% after enhancement. And in the NIST fingerprint data set, the original accuracy was 42% to 88% after refinement, so the enhanced image can affect the accuracy performance. Indeks terms: fingerprint, image enhancement, two dimensions discrete wavelet transform, singular value decomposition, convolutional neural network.

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