Biometrics testing has for objective to determine the performance of a biometric system in order to guarantee security and user experience requirements. Providing trust in biometric systems is a key for many manufacturers. The performance is usually measured through the computation of matching scores between legitimate and impostor samples from a given database. Different bias in particular those linked to the environmental conditions can modify the performance of a biometric system. In this paper, we study the impact of acquisition conditions on fingerprint systems considering at the same time the quality and accuracy. We defined an own-made database controlling the acquisition conditions and we observe the behavior of three different matchers on these biometric data. Experimental results allow us to quantity their impact on performance and draw conclusions for testing biometric systems.
Ensuring security on biometric systems has always been a high priority concern. Certification of biometric systems involves the testing of the system's performance and its resistance to spoof attacks. The anti-spoofing test implies the creation and scan of multiples physical spoofs. This requests laboratory expertise and high amount of time for spoofs creation. In this paper, we propose a new solution based on deep learning to translate genuine fingerprint images and transform them into what they would look like if they were created from known spoof materials usually involved in fingerprint spoofing tests. Digitally Synthetized Fingerprint Spoofs (DSFS) help to cover a larger number of spoofs materials than it would be possible to physically fabricate in a given time. Validation method shows that synthetized images are as good as real spoofs considering their quality.
Fingerprint recognition is a common solution for user authentication in Cybersecurity. This paper deals with the context of the certification of fingerprint biometric systems. The increasing use of biometric systems makes their certification a mandatory step in their development to assess their behavior in a real situation use. It has been shown that certain parameters such as environmental conditions can have a significant impact on the performance of biometric systems. However, there are also non-controlled parameters that depend on the user's state such as the quality of his biometric samples. In this paper, we propose a study that explores the performance of fingerprint systems across these parameters.
