Gender Classification Using Convolutional Neural Network (CNN)

Authors

  • Cherukuri Vyshnavi Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India
  • Nookala Sai Homitha Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India
  • Balabhadra Vasavi Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India
  • Mareedu Bhavana Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India
  • Suneetha Bulla Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India

Keywords:

Backpropagation, Merged Convolutional Neural and Subsampling Techniques Layers, Convolutional Neural Network, Gender Classification

Abstract

The purpose of this paper is to demonstrate an innovative convolutional neural network (also known as CNN) methodology for real-time categorization of gender via face photos. The suggested CNN architecture boasts much reduced computational complexity than the current methodologies used in pattern recognition applications. By combining convolutional and subsampling layers, the overall processing layer count is minimised to four. Notably, using cross-correlation versus standard convolution tends to alleviate computing strain. The association is programmed using extended worldwide acquisition frequencies and a second-order backpropagation learning algorithmic framework. The demonstrated CNN approach has been examined using two freely downloadable facial statistics, SUMS and AT&T, with classification accuracies of 99.38% and 98.75%, respectively. Furthermore, the neural network's algorithm demonstrates exceptional efficiency by analysing and categorising a 32 by 32-pixel face picture in just 0.27 milliseconds, resulting in an outstanding consumption of over 3700 images per second. The successful performance of the proposed CNN methodology is further demonstrated by its speedy convergence throughout the training process, which requires less than 20 epochs. The results were produced to showcase the suggested CNN's outstanding accuracy in accurately categorising data, launching it as a realistic and effective real-time identification of the gender system.

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Published

24.03.2024

How to Cite

Vyshnavi, C. ., Homitha, N. S. ., Vasavi, B. ., Bhavana, M. ., & Bulla, S. . (2024). Gender Classification Using Convolutional Neural Network (CNN). International Journal of Intelligent Systems and Applications in Engineering, 12(3), 454–464. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5270

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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