AccScience Publishing / IJOSI / Volume 8 / Issue 4 / DOI: 10.6977/IJoSI.202412_8(4).0003
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Revolutionizingage and gender recognition: anenhanced CNN ar-chitecture

Rashna Sharmin Tumpa1 ,  Md. Khaliluzzaman1* ,  MD Jiabul Hoque2 ,  Roshni Tasnim1
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1 Department of Computer Science and Engineering, International Islamic University Chittagong, Chattogram-4318, Bangladesh
2 Department of Computer and Communication Engineering, International Islamic University Chittagong, Chattogram-4318, Bangladesh
IJOSI 2024 , 8(4), 27–45; https://doi.org/10.6977/IJoSI.202412_8(4).0003
Submitted: 27 November 2023 | Revised: 22 May 2024 | Accepted: 31 July 2024 | Published: 30 December 2024
© 2024 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

The recognition of age and gender in images has had a significant impact on computer vision, particularly with the increasing application of digital platforms. Deep Convolutional Neural Networks (DCNNs) show promising perfor-mance. However, they demand substantial computational resources, limiting their deployment in real systems, espe-cially those with constraints on resources or cost. This study performs a sensitivity analysis in order to show how some changes in the architecture of the network can influence the tradeoff between accuracy and performance. For that, in this work, we have investigated various CNN architectures and introduced an effective convolutional neural network (CNN) model to precisely predict gender and age attributes using the Adience dataset. Amidst unfiltered and diverse image sources from various devices, our model exhibits an impressive 92.24% accuracy across eight distinct age groups and two gender categories. The model's strength lies in its adeptness at handling intricate image data, allowing comprehensive adjustmentsto age and gender parameters. By employing advanced deep learning techniques and comparing with MiniVGGNet, our model showcases exceptional performance.

Keywords
Adience dataset
Age and Gender recognition
Computer Vision
Deep Convolutional Neural Network (DCNN)
MiniVGGNet.
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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