• Prof. Pallavi Joshi
• Khushi Hemant Dubey
• Priyadarshani Arvind Patil
• Pouravi Sudhir Raut

This project focuses on Age and gender classification is a fundamental task in computer vision with numerous applications in fields such as marketing, healthcare, and security. Support Vector Machine (SVM) is a popular machine learning algorithm that has proven to be effective for solving classification problems. In this study, we propose a novel approach for age and gender classification using Support Vector Machine, which leverages facial features as input data. Our approach begins by extracting relevant facial features from input images, including facial landmarks, texture patterns, and color information. These features are preprocessed to ensure consistency and reduce noise. Subsequently, a Support Vector Machine classifier is trained on a labeled dataset consisting of age and gender information. We employ a multi-class SVM approach to simultaneously classify images into different age and gender categories. Our SVM classifier is optimized for accuracy, and a comprehensive evaluation is performed using a variety of metrics, such as precision, recall, and F1-score. The experimental results demonstrate the effectiveness of the proposed approach in accurately classifying both age and gender, outperforming other traditional classification methods.

Support Vector Machine(SVM), Age and gender classification

"Age and Gender Classification using Support Vector Machine", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.64-72, July-2024, Available :http://www.jetir.org/papers/JETIRGK06008.pdf

"Age and Gender Classification using Support Vector Machine", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pp64-72, July-2024, Available at : http://www.jetir.org/papers/JETIRGK06008.pdf