Detection and Classification of Human Gender into Binary (Male and Female) Using Convolutional Neural Network (CNN) Model

Gift Adene *

Department of Computer Science, Akanu Ibiam Federal Polytechnic, Unwana, Nigeria.

Nwankpa Joshua Makuo

Nnamdi Azikiwe University, Awka, Nigeria.

Chukwuogo Okechukwu Ejike

Nnamdi Azikiwe University, Awka, Nigeria.

Ikedilo Obiora Emeka

Department of Computer Science, Akanu Ibiam Federal Polytechnic, Unwana, Nigeria.

Chinedu Emmanuel Mbonu

Nnamdi Azikiwe University, Awka, Nigeria.

*Author to whom correspondence should be addressed.


This paper focuses on detecting the human gender using Convolutional Neural Network (CNN). Using CNN, a deep learning technique used as a feature extractor that takes input photos and gives values to various characteristics of the image and differentiates between them, the goal is to create and develop a real-time gender detection model. The model focuses on classifying human gender only into two different categories; male and female. The major reason why this work was carried out is to solve the problem of imposture. A CNN model was developed to extract facial features such as eyebrows, cheek bone, lip, nose shape and expressions to classify them into male and female gender, and also use demographic classification analysis to study and detect the facial expression. We implemented both machine learning algorithms and image processing techniques, and the Kaggle dataset showed encouraging results.

Keywords: Gender detection, convolutional neural network, deep learning, artificial intelligence

How to Cite

Adene, G., Makuo, N. J., Ejike, C. O., Emeka, I. O., & Mbonu, C. E. (2024). Detection and Classification of Human Gender into Binary (Male and Female) Using Convolutional Neural Network (CNN) Model. Asian Journal of Research in Computer Science, 17(6), 135–144.


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