Exploring the Feasibility of Deep Learning Techniques for Accurate Gender Classification from Eye Images
2025-08-04
Summary
The article investigates the use of deep learning, specifically Convolutional Neural Networks (CNN), for gender classification by analyzing images of the periocular region (area around the eyes). The study introduces a CNN model that achieves high accuracy rates—99% on the CVBL dataset and 96% on the "Female and Male" dataset. This model uses fewer parameters than other existing models while maintaining comparable accuracy.
Why This Matters
Understanding gender classification through periocular images can significantly benefit fields like security, surveillance, and personalized advertising, where unobtrusive and reliable identification methods are essential. The high accuracy of the proposed model suggests that focusing on the periocular region might be more effective than traditional face-based methods, especially in cases where the full face is obscured or altered.
How You Can Use This Info
Professionals in security and surveillance can leverage this technology to enhance access control systems by integrating gender recognition for added layers of security. Marketing professionals could use this approach to tailor content more precisely, even in scenarios where full face data isn't available. Moreover, the reduced computational load of the proposed model makes it a feasible option for real-time applications on devices with limited processing power.