Comparative performance of Convolutional Neural Networks Architecture for face biometric authentication system

Mat Ariff, Noor Azwana and Ismail, Amelia Ritahani and Abdul Aziz, Normaziah (2021) Comparative performance of Convolutional Neural Networks Architecture for face biometric authentication system. In: IEEE 8th International Conference on Computing, Engineering and Design (ICCED), 28-28 July 2022, Sukabumi, Indonesia.

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Abstract

Biometric authentication plays a vital role nowadays compared to password or token-based authentication. There are a lot of methods for biometric authentication algorithms that have been proposed but it can be said that the Deep Learning method give much more reliable and secure compared to other methods specifically Convolutional Neural Networks (CNN) for face recognition. Therefore, this paper will review the performance of top CNN architectures which are LeNet, AlexNet, VGGNet, GoogleNet, and ResNet by using the proposed face dataset of 7 celebrity classes where each class has 35 images that have been collected from Google Images. Data augmentation has been performed to increase the size of the dataset before it was fed into the CNN model. The experiment shows that AlexNet shows promising results compared to the other architectures on the proposed dataset.

Item Type:	Conference or Workshop Item (Plenary Papers)
Uncontrolled Keywords:	Deep learning, Image recognition, Error analysis,Face recognition, Authentication,Computer architecture, Passwords
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	03 Aug 2023 17:20
Last Modified:	03 Aug 2023 17:20
URI:	http://irep.iium.edu.my/id/eprint/105813

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