Even if the market penetration rate of biometric technologies is still far below its potential, many biometric systems are used in our daily real-life. One of the main reasons to its low proliferation is the lack of a generic and complete approach that quantifies the performance of biometric systems taking into account individuals' perception among the process. Among all the existing biometric modalities, authentication systems based on keystroke dynamics are particularly interesting. Many researchers proposed in the last decades some algorithms to increase the efficiency of this approach. Nevertheless, none significant benchmark is available and commonly used in the state of the art to compare them by using a similar and rigorous protocol. We propose in this paper: a benchmark testing suite composed of a database and a software that are available for the scientific community for the evaluation of keystroke dynamics based systems. Performance evaluation of various keystroke dynamics methods tested on the database is available in [1].

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Keystroke dynamics biometric systems have been studied for more than twenty years. They are very well perceived by users, they may be one of the cheapest biometric system (as no specific material is required) even if they are not commonly spread and used [1]. We propose in this paper a new method based on SVM learning satisfying operational conditions (no more than 5 captures for the enrollment step). In the proposed method, users are authenticated thanks to keystroke dynamics of a passphrase (that can be chosen by the system administrator). We use the GREYC keystroke benchmark that is composed of a large number of users (100) for validation purposes. We tested the proposed method face to four other methods from the state of the art. Experimental results show that the proposed method outperforms them in an operational context.

International audienceBiometric authentication methods are being increasingly used for many types of applications. Since such methods necessitate humans to interact with a device, effective implementation requires consideration of the perceptions and responses of end users. Towards this goal, we present in this paper a modalityindependent evaluation methodology to study users' acceptance and satisfaction of biometric systems. It is based on the use of a questionnaire and some data mining tools for the analysis. We have applied it on two biometric systems developed in our research laboratory. The results from this study demonstrated that users' satisfaction analysis should be more taken into account when developing biometric systems. A significant panel of 70 users was more satisfied from the keystroke system than the other one. Users surprisingly considered that its perceived performance was also better even if the used face system has a better performance with an EER of 8.76% than the keystroke one with an EER of 17.51%. The robustness of a system against attacks and its perceived trust have been identified as important factors to take into account when designing biometric systems. Results have also demonstrated significant relationships between demographic factors and their perception about the biometric technology and the studied systems

We present in this paper a study on the ability and the benefits of using a keystroke dynamics authentication method for collaborative systems. Authentication is a challenging issue in order to guarantee the security of use of collaborative systems during the access control step. Many solutions exist in the state of the art such as the use of one time passwords or smart-cards. We focus in this paper on biometric based solutions that do not necessitate any additional sensor. Keystroke dynamics is an interesting solution as it uses only the keyboard and is invisible for users. Many methods have been published in this field. We make a comparative study of many of them considering the operational constraints of use for collaborative systems.

Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.

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