Open Access

ARTICLE

Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

by Mohamed Ezz¹, Ayman Mohamed Mostafa^1,*, Ayman Elshenawy^2,3

1 College of Computer and Information Sciences, Jouf University, Sakaka, 72314, Saudi Arabia
2 Systems and Computers Engineering DEPT, Faculty of Engineering, Al-Azhar University, Cairo, 11651, Egypt
3 Software Engineering & Information Technology, Faculty of Engineering & Technology, Egyptian Chinese University, Egypt

* Corresponding Author: Ayman Mohamed Mostafa. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 3659-3675. https://doi.org/10.32604/iasc.2023.031561

Received 21 April 2022; Accepted 09 June 2022; Issue published 17 August 2022

Abstract

Face authentication is an important biometric authentication method commonly used in security applications. It is vulnerable to different types of attacks that use authorized users’ facial images and videos captured from social media to perform spoofing attacks and dynamic movements for penetrating security applications. This paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble technique. The proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a specific response for every authentication trial with a different sequence of emotions. The proposed model is applied to the KDEF dataset using 10-fold cross-validations. Several improvements are made to the proposed model. First, the VGG16 model is applied to the seven common emotions. Second, the system usability is enhanced by analyzing and selecting only the four common and easy-to-use emotions. Third, the horizontal ensemble technique is applied to enhance the emotion recognition accuracy and minimize the error during authentication processes. Finally, the Horizontal Ensemble Best N-Losses (HEBNL) is applied using challenge-response emotion to improve the authentication efficiency and minimize the computational power. The successive improvements implemented on the proposed model led to an improvement in the accuracy from 92.1% to 99.27%.

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Keywords

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