Dorsal Hand Veins Based Biometric Identification System Using Deep Learning

Year 2021, Volume: 14 Issue: 1, 1 - 15, 31.03.2021

Masoumeh Zehtab Nayebi Zeynep Turgut

https://doi.org/10.18185/erzifbed.848004

Abstract

Identification systems have become biometric-based, especially with the increase in the performance rates of machine learning methods. Biometric identification systems offer a high level of security by using reliable, difficult-to-change parameters. In this thesis, a biometric identification system is proposed using dorsal hand vein patterns. The relevant system has been tested on the sample dataset in the literature. The number of data were increased by adding noisy data to the data set used. The classification was made on the preprocessed images using SVM, ANN, LDA + KNN, and CNN methods. It has been determined that the highest identification accuracy is achieved when CNN is used, and CNN method provides higher performance compared to other methods. With the proposed identification system, after multiple runs, an average accuracy of 99.64% is achieved with the CNN machine learning method.

Keywords

deep learning, electronics, biometric identification, hand veins, electronic system

Dorsal Hand Veins Based Biometric Identification System Using Deep Learning

Abstract

Identification systems have become biometric based, especially with the increase in the performance rates of machine learning methods. Biometric identification systems offer a high level of security by using reliable, difficult-to-change parameters. In this study, a biometric identification system is proposed using dorsal hand vein patterns. The relevant system has been tested on the sample dataset in the literature. The number of data were increased by adding noisy data to the data set used. Classification was made on the preprocessed images using SVM, ANN, LDA + KNN, and CNN methods. It has been determined that the highest identification accuracy is achieved when CNN is used, and CNN method provides higher performance compared to other methods. With the proposed identification system, after multiple runs, an average accuracy of 99.64% is achieved with the CNN machine learning method.

Keywords

deep learning,, electronics, biometric identification, hand veins, electronic system

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