Hybrid Deep Learning Framework for Facial Image Synthesis and Reconstruction Using StyleGAN2 and Autoencoders

Realistic human face synthesis and reconstruction are important tasks in computer vision, entertainment, biometrics, identity verification and so on. In this work, a hybrid deep learning approach is studied that combines the generative ability of StyleGAN2 with the feature learning and reconstruction ability of a convolutional autoencoder. The main aim is to analyse the performance of an autoencoder to compress and reconstruct high quality synthetic facial images while preserving the main structural and perceptual features.Instead of using traditional real-world paired datasets, this system takes the synthetic face images generated by StyleGAN2 as the training input. The generated images provide a large and controlled dataset, allowing the model to learn facial representations without privacy concerns or data collection limitations. The synthetic images are then inputted to a convolutional autoencoder, which encodes the images into a small latent space and then reconstructs the images. The project shows how generative models and representation learning can be combined in one pipeline. It demonstrates effective utilisation of self-generated datasets for training deep-learning models, reducing the demand for large annotated datasets. The results show the model’s ability to keep the facial structure and image quality through the reconstruction process. This shows the possibility of combining the GAN-based data generation and the autoencoder-based learning for efficient image synthesis and reconstruction applications.