You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Keystroke Dynamics Biometrics 
Chapter Title:  Score Fusion. 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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.

Literature Review:
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 .