A Survey of Keystroke Dynamics Biometrics <s> Numerical Data Input. <s> The progress in the field of human-computer interaction with hand held electronic devices, such as, personal digital assistants (PDAs) and mobile phones searches for new interaction techniques. Proximity sensing extends the concept of computer-human interaction beyond actual physical contact with a device. In this paper, a virtual keyboard implementation is presented and keystroke recognition experiments with the keyboard utilizing proximity measurements are described. An infrared (IR) transceiver array is used for detecting the proximity of a finger. Keystroke recognition accuracy is examined with k-nearest neighbor (k-NN) classifier while a multilayer perceptron (MLP) classifier is designed for online implementation. Experiments and results of keystroke classification are presented for both classifiers. The recognition accuracy, which is between 78% and 99% for k-NN classifier and between 69% and 96% for MLP classifier, depends mainly on the location of a specific key on the keyboard area. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Numerical Data Input. <s> 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. <s> BIB002
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