A Modified CNN for Age and Gender Prediction

Age and gender information are essential for many real-world applications, such as social intelligence, biometric identity verification, video surveillance, human-computer interaction, digital consumer, crowd behavior analysis, online marketing, item recommendation, and many more. This study intends to employ deep learning technology in the prediction process, effective accuracy, and predictive mining and assess it in order to obtain the best outcomes of prediction and get around the issues of time, accuracy, and processing load. In this multi-task learning problem, age and gender are predicted concurrently with the help of a single Convolutional neural network with two heads (output branches). The model has 95% accuracy for gender classifier and 92% accuracy for age classifier. The pro-posed model uses the computing resources (RAM, CPU, and GPU) in a much more optimized manner and the computing cost is also lower.