Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings
Pietro Melzi, Hatef Otroshi Shahreza, Christian Rathgeb, Rubén Tolosana, Rubén Vera-Rodríguez, Julián Fiérrez, Sébastien Marcel, Christoph Busch
Abstract
This study focuses on the protection of soft-biometric at-tributes related to the demographic information of individ-uals 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 at-tributes 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 simultane-ously secure multiple soft-biometric attributes and support the application of embedding domain transformations be-fore addressing the enhancement of soft-biometric privacy.