Dissecting Facial Features: A Deep Learning Synthesis for Age and Gender Prediction

Age and gender detection is crucial for enhancing user experience and security in applications like personalized content delivery and demographic analysis. However, current methods often struggle with accuracy due to diverse facial features and challenging imaging conditions. This research proposes a deep learning-based approach that uses ResNet-50 and Convolutional Neural Networks (CNN) to address these issues. We trained our model on the UTKFace dataset, leveraging residual networks to enhance feature extraction, resulting in higher accuracy and efficiency in real-world scenarios. According to experimental results, ResNet-50 performs better than conventional CNN models in terms of accuracy and processing speed, which qualifies it for real-time applications. Our approach provides a scalable, reliable solution to age and gender detection, with the potential for broad deployment in various industries.