A Review on the Human Face Generation Approaches

Conditional Generative Adversarial Networks (CGANs) can be used in generating high-quality images while allowing for control over specific attributes, making them an ideal choice for preserving the identity of generated faces. An approach for human face generation is using CGANs with a primary focus on identity preservation. A dataset of facial images of various individuals is employed and trained the CGAN model to generate faces while maintaining the unique characteristics and identity of the selected individual. By conditioning the generator on the identity, we can control and fine-tune the generated faces to resemble the target person, thus preserving their identity. It also explores the challenges associated with identity preservation, such as ensuring diversity within a single identity, handling variations in facial expressions, and coping with varying lighting.