A Survey of Keystroke Dynamics Biometrics <s> Decision 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> Decision Fusion. <s> Keystroke dynamics refers to the timing information that expresses precisely when each key was pressed and released as a person types. In this paper, we present a novel keystroke dynamic recognition system by using a novel fusion approach. Firstly, we extract four types of keystroke latency as the feature data from our dataset. We then calculate their mean and standard deviation to be stored as template. The test feature data will be transformed into similarity scores via Gaussian Probability Density Function (GPD). We also propose a new technique, known as Direction Similarity Measure (DSM), to measure the trend differential among each digraph in a phrase. Lastly, various fusion rules are applied to improve the final result by fusing the scores produced by GPD and DSM. Best result with equal error rate of 2.791% is obtained when the AND voting rule is used. <s> BIB002
Fusion at decision level is among the simplest fusion scheme available since it has the benefit of not requiring any change to the internal structure of other modularity. Scores produced by different classification algorithms are compared against authentication threshold and generates individual preliminary authentication decision. Final decision is obtained by voting schemes such as majority BIB001 , AND, and OR voting BIB002 .