Fingerprint classification using support vector machine

Ho, Kah Weng (2020) Fingerprint classification using support vector machine. Final Year Project, UTAR.

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Abstract

Biometrics is metrics of human characteristics. The identification or verification of an individual can be done through biometrics authentication by extracting the human characteristic data. One of the most well-known and most used methods for identification is fingerprint recognition. Fingerprint recognition is the automated process of identifying the identity of an individual based on the comparison of two fingerprints. Fingerprints are completely unique to every human. Each individual’s finger has different tiny ridges, valleys and patterns. In order to reduce the search time of fingerprints in a database, classification is needed. However, the quality of the fingerprint images may vary depending on the hardware that is used to capture the images. Noise may occur when capturing a fingerprint images. In order to classify the fingerprint into different categories accurately, a noise reduction steps has to be done. In this project, a fingerprint classification method using Support Vector Machine that includes a noise reduction method is developed. The accuracy of the classifier is also compared with others that use different noise reduction method.

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