• S.Mallem Kondaiah
• Mr. P.Yejdani Khan

The accurate prediction of gender and age from various data sources, such as images, text, and other biometric data, has become a critical area of research in the fields of computer vision, machine learning, and human-computer interaction. This paper reviews state-of-the-art methodologies for gender and age prediction, highlighting key techniques including convolutional neural networks (CNNs), deep learning architectures, and ensemble methods. We discuss the preprocessing steps necessary for improving prediction accuracy, such as data augmentation and normalization, as well as the challenges posed by imbalanced datasets and diverse demographic characteristics. The paper also explores the applications of gender and age prediction systems in areas such as targeted marketing, security, and personalized user experiences. Comparative analysis of different models demonstrates the effectiveness and limitations of current approaches, providing insights into future directions for enhancing the robustness and fairness of these prediction systems. Experimental results on benchmark datasets underscore the potential of advanced algorithms to achieve high accuracy, while also emphasizing the ethical considerations and privacy concerns inherent in deploying these technologies

GENDER AND AGE PREDICTION USING WEB CAM

"GENDER AND AGE PREDICTION USING WEB CAM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.119-124, June-2024, Available :http://www.jetir.org/papers/JETIRGL06022.pdf

"GENDER AND AGE PREDICTION USING WEB CAM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp119-124, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06022.pdf