review-article

Design of gender recognition system using quantum-based deep learning

Authors:

Hande Çavşi Zaim,

Esra Nergis YolaçanAuthors Info & Claims

Neural Computing and Applications, Volume 36, Issue 4

Pages 1997 - 2014

https://doi.org/10.1007/s00521-023-09213-5

Published: 18 November 2023 Publication History

Abstract

Biometric authentication systems identify or verify a person from a digital image taken by security cameras or fingerprint readers. Digital images are used for authentication wherever a security system exists, such as in airports and banks. Although biometric data authentication boosts security, it has several practical challenges and is a difficult problem in computer vision. Another application classifies biometric data according to certain characteristics such as age, gender, or race. One of the biometric data frequently used for this purpose and has become very important is face images. Deep learning systems can learn rich, compact representations of faces from very big face datasets, allowing people to surpass their facial analysis talents. The Convolutional Neural Network (CNN) has recently obtained very promising face analysis results among these methods. Although CNN has the beneficial use of the data’s correlation information, it has trouble learning efficiently when the supplied amount of the data or model is too huge. Quantum Convolutional Neural Network (QCNN) provides a new solution to a CNN-related problem using a quantum computing environment. In this study, gender recognition is performed with CNN and QCNN algorithms, and the results are compared in terms of time and accuracy. The purpose of the study is to show the comparative evaluation of QCNN and its classical counterpart CNN algorithms with detailed applications under the same conditions. 92% accuracy for QCNN and 90% accuracy for CNN are obtained. The total processing time is 128.85 s for QCNN and 832.30 s for CNN.

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Cited By

Kumar GRao P(2024)Modified hybrid fruit fly-based salp swarm optimization strategy for energy efficient optimization in MIMO Wireless Powered Communication NetworksJournal of High Speed Networks10.3233/JHS-23017330:4(535-555)Online publication date: 15-Oct-2024
https://dl.acm.org/doi/10.3233/JHS-230173

Index Terms

Design of gender recognition system using quantum-based deep learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Security services
    1. Authentication

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 36, Issue 4

Feb 2024

593 pages

EISSN:1433-3058

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 November 2023

Accepted: 21 October 2023

Received: 12 May 2022

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Cited By

Kumar GRao P(2024)Modified hybrid fruit fly-based salp swarm optimization strategy for energy efficient optimization in MIMO Wireless Powered Communication NetworksJournal of High Speed Networks10.3233/JHS-23017330:4(535-555)Online publication date: 15-Oct-2024
https://dl.acm.org/doi/10.3233/JHS-230173

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