A Survey of Keystroke Dynamics Biometrics <s> Other Hardware. <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> Other Hardware. <s> In traditional Point of Sales (POS) device, the legitimate user is mainly authenticated by PIN (Personal ::: Identification Number). PIN method has drawbacks like easy to be forgotten. Fingerprint technology, ::: as the most viable biometrics in the special and restricted hardware environment of POS device, has ::: been used to increase user convenience. However, due to inherent problems, fingerprint alone can ::: hardly replace PIN in many high secure applications. Although keystroke pattern as one kind of ::: biometrics is in research for years, no real satisfied results have been got due to its inherent great ::: variability. In this paper, we proposed an innovative method by adapting and increase keystroke ::: pattern technology to POS device and applications. Together with fingerprint system, the adapted ::: keystroke pattern recognition system constitute a multibiometric system, which helps achieve an ::: increase in performance, and provide anti-spoofing measures by making it difficult for an intruder to ::: spoof multiple biometric traits simultaneously. Since this approach is a combination of partially featurebased ::: and partially knowledge-based, it can be a more deployable alternative to strengthen existing ::: PIN and pure biometric authentication methods. Experimental results support that this approach can ::: be better trade-off of the security and user convenience. <s> BIB002
Although keyboard, num-pad and mobile phone have been the dominating input devices for keystroke dynamics research, some works have also been performed on less common equipment. For instance, four pairs of infrared proximity-sensing devices were used to project a virtual numeric keyboard on a piece of white surface BIB001 . In the experiment, user's finger has to be held at a 90 degree angle to the surface keyboard for proper detection. Therefore, with the increase complication of input procedure, the usability has been a cause of doubt. Conversely, BIB002 implemented a more practical multimodal authentication by combining keystroke dynamics input and fingerprint by using a portable point of sales device.