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Binary Gender Classification of African Fingerprints using CNN

Maruthukunnel Jacob, John (2023) Binary Gender Classification of African Fingerprints using CNN. Masters thesis, Dublin, National College of Ireland.

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Fingerprints, one of the most popular biometric authenticators, can distinguish between genders. The primary difficulty in fingerprint classification is time-effective models. In this paper, the author compares the binary gender classification performance of VGG-19, VGG-16, InceptionV3, and ResNet-50 for fingerprints. Traditional deep networks perform poorly and take longer to train because of their narrow depth range. In addition, they have to deal with model overfitting because there is a lack of fingerprint data. To solve the issues with insufficient fingerprint data, data augmentation techniques rotate, zoom, and flip is implemented. Transfer learning is used to pre-train the four CNNs to accelerate model training. Evaluation of the proposed model is done by testing, training accuracy, and loss. The VGG-19 model performed the best with a testing accuracy of 71.9% along with VGG-16 with 72.3% in comparison to InceptionV3 with 67.3%, and ResNet-50 with the lowest accuracy of 60.8%.

Item Type: Thesis (Masters)
Muntean, Cristina Hava
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Biometric Identification
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 19 May 2023 16:41
Last Modified: 19 May 2023 16:41

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