Age Estimation and Gender Classification from Facial Images

This study addresses the task of simultaneous age estimation and gender classification from facial images using convolutional neural networks (CNNs). The objective was to develop a unified model capable of handling both regression and classification tasks effectively. Four models with varying architectures, loss functions, and preprocessing strategies were implemented and evaluated. The best-performing model achieved over 90% accuracy in gender classification and a mean absolute error (MAE) below four years for age estimation. Performance analysis showed variation across age groups, with reduced accuracy for elderly individuals due to dataset imbalance and improved predictions for younger and middle-aged adults. To assess generalization, the model was also tested on external images, maintaining strong performance, particularly in gender classification. Challenges such as overfitting and face misdetection were addressed through preprocessing adjustments and model tuning. Beyond empirical results, this work consolidates a unified, reproducible protocol for joint age estimation and gender classification on a widely used face database. We standardize preprocessing, implement a consistent image-level split with a published seed, and define task-appropriate metrics. All training details are documented to provide a baseline, enhanced by a qualitative error analysis, enabling consistent reporting and facilitating future extensions. This approach demonstrates the effectiveness of CNNs for age and gender prediction and highlights their potential integration into recommendation systems that personalize content based on demographic attributes.