A Survey of Keystroke Dynamics Biometrics <s> Score Fusion. <s> In this article, we present three methods for the keystroke dynamic authentication problem. We use in the first method, the average and the standard deviation, in the second the rhythm of striking and in the third, a comparison of the times order. After having presented these methods, we propose to realize a fusion of them. The results obtained indicate good performance of each method alone, as well as a significant improvement of performance with fusion, from 3.43% of EER for the best method alone down-to 1.8% with fusion. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Score Fusion. <s> A new approach for keystroke-based authentication when using a cellular phone keypad as input device is presented. In the proposed method, users are authenticated using keystroke dynamics acquired when typing fixed alphabetic strings on a mobile phone keypad. The employed statistical classifier is able to perform user verification with an average equal error rate of about 13%. The obtained experimental results suggest that, when using mobile devices, a strong secure authentication scheme cannot rely on the sole keystroke dynamics, which however can be a module of a more complex system including, as basic security, a password-based protocol eventually hardened by keystroke analysis. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Score Fusion. <s> This paper describes a novel technique to strengthen password authentication system by incorporating multiple keystroke dynamic information under a fusion framework. We capitalize four types of latency as keystroke feature and two methods to calculate the similarity scores between the two given latency. A two layer fusion approach is proposed to enhance the overall performance of the system to achieve near 1.401% Equal Error Rate (EER). We also introduce two additional modules to increase the flexibility of the proposed system. These modules aim to accommodate exceptional cases, for instance, when a legitimate user is unable to provide his or her normal typing pattern due to reasons such as hand injury. <s> BIB003
Score level fusion combines output scores from different classifiers prior to decision making. Since output scores generated from different classifiers may not always be in a unified range; therefore, it is essential to normalize the scores before fusion BIB001 . A commonly used normalization method includes maximum difference between score, zscore, tanh-estimator, and double sigmoid BIB002 . However, not all score level fusions require prior normalization. For instance in experiment BIB003 , score produced by Gaussian probability density function and direction similarity measure are both readily within the same range of 0 and 1; hence, normalization is unnecessary. Combining scores from different matchers usually involves fusion rules. Simple and common rules found in the literature include weighted sum  , maximum and minimum score selection, median, product rule, and sum rule BIB001 .