Advances in Gender Classification from Facial Images Using CNNs: A Comprehensive Review

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Digital Communication and Analog Signals
Received Date: 10/24/2024
Acceptance Date: 11/25/2024
Published On: 2024-11-20
First Page: 1
Last Page: 5

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By: Anjlee Dhurve and Mayank Pathak

1- Scholar, Department of Computer Science and Engineering, Technocrats Institute of Technology, Bhopal, Madhya Pradesh
2- Professor, Department of Computer Science and Engineering, Technocrats Institute of Technology, Bhopal, Madhya Pradesh

Abstract

Predicting age and gender from facial images is important for various uses, like social interactions, security, and customizing user experiences. Despite progress in facial recognition technology, getting accurate and reliable predictions is still a challenge. This paper reviews recent advancements in Convolutional Neural Networks (CNNs) for age and gender classification, comparing their effectiveness to traditional methods. CNNs excel in extracting features from images and have shown notable improvements in accuracy because they can learn relevant features automatically. The review covers several CNN-based methods, such as the Evolutionary-Fuzzy-Integral CNN (EFI-CNN), hybrid CNN-Extreme Learning Machine (ELM) models, and other sophisticated architectures. These approaches tackle issues like image quality, misalignment, and occlusion, resulting in better classification performance. Additionally, the paper explores how CNNs are used in broader image analysis tasks, highlighting their strengths in pattern recognition and efficiency. Overall, the integration of CNNs has significantly advanced age and gender classification, demonstrating their real-world potential.

Keywords: Convolutional Neural Networks (CNNs), age classification, gender classification, feature extraction, deep learning, transfer learning, hybrid models.

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How to cite this article: Anjlee Dhurve and Mayank Pathak, Advances in Gender Classification from Facial Images Using CNNs: A Comprehensive Review. International Journal of Digital Communication and Analog Signals. 2024; 10(02): 1-5p.

How to cite this URL: Anjlee Dhurve and Mayank Pathak, Advances in Gender Classification from Facial Images Using CNNs: A Comprehensive Review. International Journal of Digital Communication and Analog Signals. 2024; 10(02): 1-5p. Available from:https://journalspub.com/publication/ijdcas/article=12130
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