In contemporary scenarios, privacy is paramount, especially in applications such as video interviews.This study introduces a privacy-focused real-time face and hair swapping method designed to conceal identity while retaining essential facial attributes of the original subject.Unlike conventional face swapping methods that rely on a reference image of a realistic person, our proposed method eliminates this need for enhanced Play Equipment privacy.
Instead, this work introduces the use of a synthetic face image generated by a Generative Adversarial Networks (GANs), offering a secure solution that addresses the heightened importance of privacy in face and hair swapping applications, particularly in sensitive contexts like interviews.The proposed method in this study is an efficient 3-phase pipeline capable of performing these operations in real-time from a standard camera.This novel approach ensures a seamless integration of anonymity and attribute preservation, Halters paving the way for more discreet and privacy-centric real-time face and hair swapping technologies, not only in video interviews but also in entertainment and communication applications such as video teleconferencing and streaming media.