Privacy-Preserving Face and Hair Swapping in Real-Time With a GAN-Generated Face Image
Dinh Tuan Tran, Duc Tung Phung, Duc Manh Duong, Katsumi Inoue, Joo‐Ho Lee, Anh Quang Nguyen · IEEE Access · 2024
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 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, 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.