Age and Gender Prediction Using Swin Transformer and Multitasking Learning

Age and gender prediction from facial images is an essential task in applications such as security systems human-computer interaction and personalized recommendations however variations in facial features due to lighting expressions and aging effects make it a challenging problem traditional convolutional neural networks CNNs often struggle with generalization whereas transformer-based models have shown superior performance by capturing long-range dependencies through self-attention mechanisms a multi-task learning approach where age estimation gender classification and contextual age positioning are trained together enhances feature representation and improves accuracy incorporating feature reweighting techniques allows the model to focus on critical facial attributes refining predictions dynamically additionally leveraging contextual learning such as relative age positioning strengthens the models ability to understand relationships between different age groups evaluations using benchmark datasets with diverse demographic distributions demonstrate the effectiveness of such an approach with performance measured through metrics like mean absolute error MAE for age estimation and classification accuracy for gender prediction future research can further enhance these models by integrating domain adaptation techniques and optimizing computational efficiency for real-time applications in biometric authentication healthcare and social media analytics