Analysis of Optimizers on AlexNet Architecture for Face Biometric Authentication System

Nowadays, biometric authentication is more important than a password or token-based authentication. There have been many techniques suggested for biometric authentication algorithms, however, it can be observed that the Deep Learning approach is significantly more effective and secure than other met...

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Bibliographic Details
Published in2022 International Conference on Information Technology Research and Innovation (ICITRI) pp. 24 - 29
Main Authors Mat Ariff, Noor Azwana, Ismail, Amelia Ritahani, Aziz, Normaziah Abdul, Amir Hussin, Amir Aatieff
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.11.2022
Subjects
Authentication
Biological system modeling
Convolutional Neural Networks
Deep learning
Deep Learning Optimizers
Face Biometric Authentication
Face recognition
Internet
Passwords
Technological innovation
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DOI10.1109/ICITRI56423.2022.9970238

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Summary:Nowadays, biometric authentication is more important than a password or token-based authentication. There have been many techniques suggested for biometric authentication algorithms, however, it can be observed that the Deep Learning approach is significantly more effective and secure than other methods, specifically Convolutional Neural Networks (CNN) with AlexNet architecture for face recognition. However, an optimization technique is crucial in the Deep Learning models so, this paper will analyze the best optimizers for AlexNet architecture which are SGD, AdaGrad, RMSProp, AdaDelta, Adam, and AdaMax by using the proposed face dataset includes 7 celebrity classes, each with 35 images obtained from Google Images. To enhance the size of the dataset, data augmentation was employed before it was fed into the AlexNet model. The experiment shows AdaMax performs well when compared to the other optimizers on the proposed dataset.
DOI:10.1109/ICITRI56423.2022.9970238