DESIGN AND IMPLEMENTATION OF A GENDER-BASED FACIAL RECOGNITION SYSTEM USING MACHINE LEARNING MODEL
OYETOSO MODUPEOLU OPEYEMI, Busari Olukayode Ayodeji, AKANJI OLULEYE O, Afenifere Yusuf Babatunde · International Journal of Nature and Science Advance Research · 2025
This paper presents a gender-based facial recognition system, which utilizes advanced machine learning algorithms and real-time image processing to address the challenges of accurate and efficient gender classification. The system leverages Convolutional Neural Networks (CNNs) for feature extraction and TensorFlow for training and optimization, ensuring high performance in various real-world conditions. OpenCV is integrated to enhance face detection and processing, enabling real-time recognition. This system offers applications in security, access control, and biometric authentication, reducing verification time and enhancing accuracy compared to traditional methods. The research discusses the significance of gender recognition in modern biometric systems and explores existing literature on the evolution and challenges of facial recognition technology. Despite its effectiveness, the system faces limitations, such as performance degradation with varying test images and slower processing speeds. Future work will focus on improving the system's robustness, handling scale and rotation variations, and transitioning to a network-based model for greater scalability and accessibility.