Title: Classification of Male and Female Eyes Using Deep Learning: A Comparative Evaluation
Authors: Shahd Albadrasawi, Mohammed Almzainy, Faten el kahlout and Samy S. Abu-Naser
Volume: 9
Issue: 1
Pages: 42-46
Publication Date: 2025/01/28
Abstract:
This study investigates the application of convolutional neural networks (CNNs) to the task of classifying male and female eyes. Using a dataset of eye images, the research explores the potential of deep learning to accurately distinguish between the genders based solely on eye features. The proposed CNN model achieved 94% accuracy on the training set and 91% on the validation set. The study addresses the challenges and limitations in feature extraction from eye images and compares the proposed model with traditional machine learning approaches. The results demonstrate the model's robustness, providing significant insights into gender recognition through partial facial analysis.