A Survey of Keystroke Dynamics Biometrics <s> Feature Fusion. <s> This paper presents a novel biologic verification method based on pressure sensor keyboards and classifier fusion techniques. The pressure sensor keyboard is a new product that occurs in the market recently. It produces a pressure sequence when keystroke occurs. The analysis of the pressure sequence should be a novel research area. In this paper, we use the pressure sequence and traditional keystroke dynamics in user authentication. Three methods (global features of pressure sequences, dynamic time warping, and traditional keystroke dynamics) are proposed for the authentication task. We combined the three methods together using a classifier fusion technique at last. Several experiments were performed on a database containing 5000 samples of 100 individuals and the best result were achieved utilizing all the method, obtaining an equal error rate of 1.41%. To make a comparison, the equal error rate is 2.04% when we use only the traditional keystroke dynamics. This approach can be used to improve the usual login-password authentication when the password is no more a secret <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Feature Fusion. <s> Among all the existing biometric modalities, authentication systems based on keystroke dynamics present interesting advantages. These solutions are well accepted by users and cheap as no additional sensor is required for authenticating the user before accessing to an application. In the last thirty years, many researchers have proposed, different algorithms aimed at increasing the performance of this approach. Their main drawback lies on the large number of data required for the enrollment step. As a consequence, the verification system is barely usable, because the enrollment is too restrictive. In this work, we propose a new method based on the Support Vector Machine (SVM) learning satisfying industrial conditions (i.e., few samples per user are needed during the enrollment phase to create its template). In this method, users are authenticated through the keystroke dynamics of a shared secret (chosen by the system administrator). We use the GREYC keystroke database that is composed of a large number of users (100) for validation purposes. We compared the proposed method with six methods from the literature (selected based on their ability to work with few enrollment samples). Experimental results show that, even though the computation time to build the template can be longer with our method (54 s against 3 s for most of the others), its performance outperforms the other methods in an industrial context (Equal Error Rate of 15.28% against 16.79% and 17.02% for the two best methods of the state-of-the-art, on our dataset and five samples to create the template, with a better computation time than the second best method). <s> BIB002
The combination of different variants of keystroke feature data is one of the most common fusion methods employed. For example, BIB002 concatenated four The Scientific World Journal 13 different keystroke durations and latencies forming a large timing vector instead of using them individually. On the other hand, BIB001 merged user typing pressure information and traditional keystroke timing data and obtained a better result as compared to using them separately.