Age & Gender Prediction

Developed a deep learning model using Convolutional Neural Networks (CNN) to predict both the gender (classification) and age (regression) of individuals from facial images. The model was trained and evaluated using the UTKFace dataset. The architecture includes a shared convolutional base and two output heads: one for binary classification (gender) and another for regression (age). The system achieved a gender prediction accuracy of 93.2% and a mean absolute error of 4.6 years for age estimation.