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:  Numerical Data Input. 
1. Abstract of Cited Paper (BIB001):  In everyday life many actions are secured and/or authenticated by use of a secret personal identification number (PIN). This paper reports an improvement to the security afforded by a numeric PIN system through the addition of a covert biometric identification process. This biometric identification process is achieved through the use of a modified keypad incorporating force sensors within the individual keys, these sensors permit the dynamic measurement of applied force and key-press timings, providing an advantage over existing keystroke dynamic techniques which rely on time-domain information only. Details of the experimental setup used are given, and the results from initial tests are reported. In these tests an equal error rate of approximately 10% is obtained and a false acceptance rate of15% with a corresponding false rejection rate of0% has been shown.

Literature Review:
As discussed in earlier section, previous studies suggested that complexity and length of input show a direct relationship with the proficiency of keystroke dynamics recognition. Input device such as those embedded in ATM machine, access control panel, and card payment machine do not have the luxury of alphabetic input. Therefore, the ability to select complex secret phrase combination will significantly be limited. Moreover, such input devices usually require only 4 (credit or debit card PIN) to 10 (numeric PIN code) length of numeric digits. Thus, it is interesting to see how keystroke dynamics recognition performs exclusively with numerical inputs. Table 9 lists a summary of research works on keystroke numeric inputs. A keypad that looked and felt exactly as the one that was deployed on commercial ATM machines has been adapted by BIB002 . Euclidean distance measure was used to calculate the difference between test vectors. A remarkable FRR of 0% was achieved at 15% for FAR. On the other hand, BIB001 abandoned physical keyboard by introducing four pairs of infrared sensors to project a virtual numeric grid keyboard. In the experiment, user's finger had to be held at a 90 degree angle to the surface of the keyboard. A 78-99% classification accuracy was reported by using -nearest neighbor classifier and multilayer perceptron. The feasibility of the sensor keyboard in real life has been called into question. We could not make a clear cut conclusion if a greater length of digit produces better result due to the small difference between the length of inputs. Hence, experiment on longer numeric length (e.g., 16 digits) that