Published Paper Details:

In recent years, machine learning techniques have revolutionized the field of computer vision, particularly in the detection of gender and age from visual data. This paper presents a comprehensive analysis of two popular machine learning models, Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), for gender and age detection tasks. The study evaluates the performance of SVM and CNN algorithms on diverse datasets and assesses their effectiveness in real-world applications such as advertising, security, healthcare, and surveillance. Results indicate that CNN-based approaches generally outperform SVM in terms of accuracy and robustness, owing to their ability to automatically learn hierarchical features from raw image data. However, SVM models exhibit advantages in computational efficiency and interpretability. The findings underscore the significance of gender and age detection in various domains, including personalized advertising, enhanced security measures, and improved healthcare diagnostics and treatments. This research contributes to the ongoing discourse on leveraging machine learning advancements for societal benefit and underscores the importance of choosing appropriate algorithms for specific applications in gender and age detection.