Security of Biometric Recognition Frameworks is Enhanced by Image Quality Assessment

Abstract

To ensure the actual presence of a real legitimate trait in contrast to a fake selfmanufactured synthetic or reconstructed sample is a significant problem in biometric
authentication, which requires the development of new and efficient protection measures.
In this paper, we present a novel software-based fake detection method that can be used in
multiple biometric systems to detect different types of fraudulent access attempts. The
objective of the proposed system is to enhance the security of biometric recognition
frameworks, by adding likeness assessment in a fast, user-friendly, and non-intrusive
manner, through the use of image quality assessment. The proposed approach presents a
very low degree of complexity, which makes it suitable for real-time applications, using 25
general image quality features extracted from one image (i.e., the same acquired for
authentication purposes) to distinguish between legitimate and impostor samples. The
experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D
face, show that the proposed method is highly competitive compared with other state-ofthe-art approaches and that the analysis of the general image quality of real biometric
samples reveals highly valuable information that may be very efficiently used to
discriminate them from fake traits