Presentation
5 October 2023 Measuring transform invertibility with computational metrics
Author Affiliations +
Abstract
Modern face ID systems are often plagued with loss of privacy. To address this, some face ID systems incorporate image transformations in the detection pipeline. In particular, we consider transforms that convert human face images to non-face images (such as landscape images) to mask sensitive and bias-prone facial features and preserve privacy, while maintaining identifiability. We propose two metrics that study the effectiveness of face image transformations used in privacy-preserving face ID systems. These metrics measure the invertibility of the transformations to ensure the meta-data of the face (e.g. race, sex, age, etc.) cannot be inferred from the transformed image.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Awroni Bhaduri, Hector Santos-Villalobos, and Suhas Sreehari "Measuring transform invertibility with computational metrics", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750F (5 October 2023); https://doi.org/10.1117/12.2677717
Advertisement
Advertisement
KEYWORDS
Face image reconstruction

Back to Top