TY  - JOUR
T1  - Dorsal Hand Veins Based Biometric Identification System Using Deep Learning
TT  - Dorsal Hand Veins Based Biometric Identification System Using Deep Learning
AU  - Turgut, Zeynep
AU  - Zehtab Nayebi, Masoumeh
PY  - 2021
DA  - March
DO  - 10.18185/erzifbed.848004
JF  - Erzincan University Journal of Science and Technology
PB  - Erzincan Binali Yildirim University
WT  - DergiPark
SN  - 2149-4584
SP  - 1
EP  - 15
VL  - 14
IS  - 1
LA  - en
AB  - 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.
KW  - deep learning
KW  - electronics
KW  - biometric identification
KW  - hand veins
KW  - electronic system
N2  - 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.
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UR  - https://doi.org/10.18185/erzifbed.848004
L1  - https://dergipark.org.tr/en/download/article-file/1472017
ER  -