Can Facial Uniqueness be Inferred from Impostor Scores?
This addresses a reliability issue in biometric systems for security and identification applications, but it is incremental as it critiques an existing method.
The paper tackled the problem of inferring facial uniqueness from impostor similarity scores in biometrics, showing that these measures are highly unstable under image quality variations like pose, noise, and blur, with experimental demonstration of instability for a specific measure.
In Biometrics, facial uniqueness is commonly inferred from impostor similarity scores. In this paper, we show that such uniqueness measures are highly unstable in the presence of image quality variations like pose, noise and blur. We also experimentally demonstrate the instability of a recently introduced impostor-based uniqueness measure of [Klare and Jain 2013] when subject to poor quality facial images.