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 review on the public benchmark databases for static keystroke dynamics 
Chapter Title:  Distribution of the Population and Samples 
1. Abstract of Cited Paper (BIB001):  In keystroke dynamics-based authentication, novelty detection methods are used since only the valid user's patterns are available when a classifier is first constructed. After a while, however, impostors' keystroke patterns become available from failed login attempts. We propose to employ the retraining framework where a novelty detector is retrained with the impostor patterns to enhance authentication accuracy. In this paper, learning vector quantization for novelty detection and support vector data description are retrained with the impostor patterns. Experimental results show that retraining improves the authentication performance and that learning vector quantization for novelty detection outperforms other widely used novelty detectors. 
2. Abstract of Cited Paper (BIB002):  Keystroke dynamics allows to authenticate individuals through their way of typing on a computer keyboard. In this study, we are interested in static shared secret keystroke dynamics (all the users type the same password). We present new soft biometrics information which can be extracted from keystroke typing patterns: the gender of the user. This is the first study, to our knowledge, experimenting such kind of information in the field of keystroke dynamics. We present a method for gender recognition through keystroke dynamics with more than 91% of accuracy, on the tested dataset, and we show the improvement on keystroke dynamics authentication method using such kind of information through pattern and score fusion. We obtain a gain of 20% when using gender information against a classical keystroke dynamics method. 
3. Abstract of Cited Paper (BIB003):  Keystroke dynamics is a viable and practical way as an addition to security for identity verification. It can be combined with passphrases authentication resulting in a more secure verification system. This paper presents a new soft biometric approach for keystroke dynamics. Soft biometrics traits are physical, behavioral or adhered human characteristics, which have been derived from the way human beings normally distinguish their peers (e.g. height, gender, hair color etc.). Those attributes have a low discriminating power, thus not capable of identification performance. Additionally, they are fully available to everyone which makes them privacy-safe. Thus, in this study, it consists of extracting information from the keystroke dynamics templates with the ability to recognise the hand(s) used (i.e. one/two hand(s)); the gender; the age category; and the handedness of a user when he/she types a given password or passphrase on a keyboard. Experiments were conducted on a keystroke dynamics database of 110 users and our experimental results show that the proposed methods are promising. 
4. Abstract of Cited Paper (BIB004):  Among all the existing biometric modalities, authentication systems based on keystroke dynamics are particularly interesting for usability reasons. Many researchers proposed in the last decades some algorithms to increase the efficiency of this biometric modality. Propose in this paper: a benchmark testing suite composed of a database containing multiple data (keystroke dynamics templates, soft biometric traits ...), which will be made available for the research community and a software that is already available for the scientific community for the evaluation of keystroke dynamics based systems. We also built the proposed biometric database on soft biometric traits for keystroke dynamics to suit the experiment. 110 people had voluntarily participated and gave their soft biometrics data i.e. the way of typing gender age and handedness.

Literature Review:
Gender distribution. Males and females can be recognized on their way of typing using keystroke dynamics BIB003 BIB004 BIB005 . We can wonder if there are performance differences in user verification between males and females. This information can be specified using a ratio of males involved in the dataset (e.g., there are 26.2% of males). Keyboard mastering. Not all individuals are used to manipulate computers and keyboards. People not accustomed in using keyboards may worsen the recognition performance. This information can be provided by a list of typing performance specified by the user (although it is too much subjective to be reliable) (e.g., all users are professional typists). Additionally, the number of used fingers, or the presence or absence of coordination between the two hands can also be interesting. Left-handed and Right-handed. So far to our knowledge, there are few studies on this aspect BIB004 , but right-handed or left-handed people may type differently. Typing complexity can be different for these two groups of people. This information can be provided using a ratio of right-handed people involved in the dataset (e.g., there are 85.3% of right-handed people). BIB002 R stands for release and P stands for pressure Age. Elderly people may type worse than younger ones because they learn computer late or could have hand diseases. A study in 1984 (at this moment people were not used with computers as today) shows that typing behavior is different between young and old people BIB001 . So, we think it may affect recognition performance. This information can be provided using either (a) a list of age of involved people, (b) a histogram, or (c) mean age. Number of individuals involved. The number of people in the dataset is one of the most important information: the bigger the population is, the more reliable the results are. The performance can decrease when the number of individuals increases, but the confidence on the results is higher. This information can be provided with an integer (e.g., here are 135 volunteers).