Optimizing Age Estimation in Facial Images with Advanced Multi-Class Classification Techniques

Abstract

Automatic age and gender prediction from facial images is increasingly crucial for applications in
security, marketing, and social media. Existing systems often face challenges related to accuracy,
demographic generalization, and bias. This study addresses these issues by developing a deep
learning-based system utilizing Convolutional Neural Networks (CNNs) for enhanced
classification of age and gender. The key research gaps include limited accuracy, insufficient
handling of diverse data, and model bias. The proposed approach encompasses data acquisition,
preprocessing, and the design of a CNN architecture within a multi-class classification framework.
Various CNN models are evaluated, incorporating transfer learning, hyperparameter optimization,
and regularization techniques to improve performance. The system's effectiveness is assessed
through metrics such as classification accuracy, precision, recall, and robustness across different
demographic groups. Results indicate significant advancements in prediction accuracy and model
generalization compared to existing methods. The technology holds practical applications in
security, personalized marketing, and social networking. Challenges such as model bias and the
need for diverse datasets are addressed, with future research aimed at further refining the model
and expanding its applicability. This work highlights the substantial improvements deep learning
offers to facial recognition technologies.