Facial Aging Prediction over Time: Integrating Variational Autoencoders (VAEs) with 3D Convolutional Neural Networks (CNNs)

This study is dedicated to exploring new methods for facial aging prediction by integrating Variational Autoencoders (VAEs) and 3D Convolutional Neural Networks (3D CNNs). The proposed model can capture and simulate the changes in facial features over long time spans, utilizing the strengths of 3D CNNs in processing temporal and spatial features, as well as the powerful capabilities of VAEs in image generation and feature learning. Through extensive experiments and analysis, we have demonstrated the effectiveness and robustness of this model under various settings of learning rate, batch size, number of network layers, and hidden layer sizes. This study not only enhances the accuracy of facial recognition technology in dealing with long-term temporal challenges but also provides new perspectives and solutions for future research and practical applications.