Detection and Classification of Human Gender into Binary (Male and Female) Using Convolutional Neural Network (CNN) Model
Gift Adene, Nwankpa Joshua Makuo, Chukwuogo Okechukwu Ejike, Ikedilo Obiora Emeka, Chinedu Emmanuel Mbonu
Asian Journal of Research in Computer Science · pp. 135–144 · Published 22 Apr 2024
10.9734/ajrcos/2024/v17i6463Abstract
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.
Cited by 2
Adeeb Dabash, K. Basulaim, Adel Haider · 2026 International Conference on Digital Transformation, Innovation & Sustainable Development (DTISD) · 2026
C. Mbonu, K. Anigbogu, D. Asogwa · arXiv.org · 2025
Related research
- The Impact/Role of Artificial Intelligence in Anesthesia: Remote Pre-Operative Assessment and Perioperative — shares topic coverage
- Detecting Dental Caries through Captured Images Using the Machine Learning Technology Teachable Machine — shares topic coverage
- Harnessing Artificial Intelligence in Healthcare Analytics: From Diagnosis to Treatment Optimization — shares topic coverage
- Diagnostic Accuracy of Artificial Intelligence for Breast Cancer Detection: A Systematic Review — shares topic coverage
- Artificial Intelligence in the Analysis of the Fetal Genome in Utero: A Critical Review of Current Paradigms, Clinical Utility and Future Horizons — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
2
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.