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.

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

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.

Nowadays, users interact with computer systems. Behavioral biometrics consists of analyzing user's interactions for identification and verification applications. This approach could be very useful for enhancing security and improving user experience and many privacy concerns are also related. In this paper, we address the problem of user identification considering their behaviors. How efficient are classical machine learning methods on such data? What about deep learning approaches? We illustrate this work on two behavioral modalities namely human activity using smartphones and keystroke dynamics on a laptop. Since the accuracy rates of most behavioral biometrics modalities are lower than morphological ones, we consider two approaches for these modalities that can be represented as time series: classical machine learning and deep learning techniques. We intend to show that many algorithms can obtain very good performance for different modalities without any specific tuning to the considered modality. This comparative analysis allows us to show that behavioral biometrics can be used for security applications (i.e. who is accessing the company information system) but could be a privacy concern as a user could be identified while navigating on the Internet.

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.