DefinePK

Abstract

Age and gender detection is a crucial technology with applications in surveillance, healthcare, personalized marketing, and social media analytics. By analyzing facial features, machine learning algorithms can efficiently classify individuals based on age group and gender, enhancing security measures and improving user experiences. Traditional methods often struggle with accuracy due to variations in facial expressions, lighting, and occlusions. However, deep learning techniques, especially Convolutional Neural Networks (CNNs), have demonstrated remarkable improvements in classification performance by automatically learning hierarchical feature representations. This paper provides an in-depth review of machine learning techniques applied to age and gender detection, evaluating their methodologies, datasets, and effectiveness. Furthermore, this study explores the challenges in implementing these models and highlights future research directions to improve real-world applicability and accuracy