Authors: Gayatri Solanki, Abhay Mundra
Abstract: Gender identification has gained significant attention in recent years because it supports many real-world applications related to demographic analysis and human-centered systems. Gender classification refers to the automated process of predicting a person’s gender (typically male or female) based on visual appearance, most commonly from facial images. However, extracting reliable and discriminative facial features remains challenging due to variations in lighting, pose, facial expressions, occlusion, and image quality. With advances in machine learning and deep learning, automatic gender classification systems have become increasingly accurate and widely adopted across multiple domains. These systems can be useful in security and access-control environments, as well as in demographic analytics and personalized services. In certain contexts, gender identification may also be applied to manage access in gender-specific spaces and services, such as women-only transportation sections or gender-segregated facilities. This review summarizes key traditional machine learning approaches (e.g., SVM-based methods) and modern deep learning techniques (e.g., convolutional neural networks), and discusses commonly used benchmark datasets and evaluation practices for gender classification.
