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 [BibTeX] [Marc21]
Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings
Type of publication: Conference paper
Citation: Melzi_WACVW_2023
Publication status: Published
Booktitle: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
Year: 2023
Month: January
URL: https://openaccess.thecvf.com/...
Abstract: This study focuses on the protection of soft-biometric attributes related to the demographic information of individuals that can be extracted from compact representations of face images, called embeddings. We consider a state-of-the-art technology for soft-biometric privacy enhancement, Incremental Variable Elimination (IVE), and propose Multi-IVE, a new method based on IVE to secure multiple soft-biometric attributes simultaneously. Several aspects of this technology are investigated, proposing different approaches to effectively identify and discard multiple soft-biometric attributes contained in face embeddings. In particular, we consider a domain transformation using Principle Component Analysis (PCA), and apply IVE in the PCA domain. A complete analysis of the proposed Multi-IVE algorithm is carried out studying the embeddings generated by state-of-the-art face feature extractors, predicting soft-biometric attributes contained within them with multiple machine learning classifiers, and providing a cross-database evaluation. The results obtained show the possibility to simultaneously secure multiple soft-biometric attributes and support the application of embedding domain transformations before addressing the enhancement of soft-biometric privacy.
Authors Melzi, Pietro
Otroshi Shahreza, Hatef
Rathgeb, Christian
Tolosana, Ruben
Vera-Rodriguez, Ruben
Fierrez, Julian
Marcel, S├ębastien
Busch, Christoph
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  • Melzi_WACVW_2023.pdf