REAL TIME AGE AND GENDER PREDICTION USING PYTHON AND ML

Facial analysis, particularly age and gender estimation, is crucial in various intelligent applications. This study presents a CNN-based model for predicting age group and gender from single face images in real-world environments. The approach involves formulating age and gender classification as discrete annotations and training classifiers accordingly. A robust image preprocessing algorithm is devised to enhance model performance. Leveraging large-scale datasets facilitates effective training and generalization of the CNN model. Despite limited accurate labels, the proposed architecture demonstrates significant progress in face recognition using deep convolutional neural networks.