Perea-Trigo, MarinaVázquez-Enríquez, ManuelBenjumea-Bellot, Jose C.Alba-Castro, Jose L.Álvarez García, Juan Antonio2025-08-212025-08-212025-06-09Perea-Trigo, M., Vázquez-Enríquez, M., Benjumea-Bellot, J.C., Alba-Castro, J.L. y Álvarez García, J.A. (2025). Sign Language Anonymization: Face Swapping Versus Avatars. Electronics, 14 (12), 2360.https://doi.org/10.3390/electronics14122360.2079-9292https://hdl.handle.net/11441/176250The visual nature of Sign Language datasets raises privacy concerns that hinder data sharing, which is essential for advancing deep learning (DL) models in Sign Language recognition and translation. This study evaluated two anonymization techniques, realistic avatar synthesis and face swapping (FS), designed to anonymize the identities of signers, while preserving the semantic integrity of signed content. A novel metric, Identity Anonymization with Expressivity Preservation (IAEP), is introduced to assess the balance between effective anonymization and the preservation of facial expressivity crucial for Sign Language communication. In addition, the quality evaluation included the LPIPS and FID metrics, which measure perceptual similarity and visual quality. A survey with deaf participants further complemented the analysis providing valuable insight into the practical usability and comprehension of anonymized videos. The results show that while face swapping achieved acceptable anonymization and preserved semantic clarity, avatar-based anonymization struggled with comprehension. These findings highlight the need for further research efforts on securing privacy while preserving Sign Language understandability, both for dataset accessibility and the anonymous participation of deaf people in digital content.application/pdf24 p.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Sign languageAnonymizationSynthetic videoAvatarFace swappinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.3390/electronics14122360