Browsing by Author "Lai, Weng K."
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Item Design and evaluation of a pressure-based typing biometric authentication system(Nature Publishing Group, 2008-12) Eltahir, Wasil E.; Salami, Momoh-Jimoh E.; Ismail, Ahmad F.; Lai, Weng K.The design and preliminary evaluation of a pressure sensor-based typing biometrics authentication system (PBAS) is discussed in this paper. This involves the integration of pressure sensors, signal processing circuit, and data acquisition devices to generate waveforms, which when concatenated, produce a pattern for the typed password. The system generates two templates for typed passwords. First template is for the force applied on each password key pressed. The second template is for latency of the password keys. These templates are analyzed using two classifiers. Autoregressive (AR) classifier is used to authenticate the pressure template. Latency classifier is used to authenticate the latency template. Authentication is complete by matching the results of these classifiers concurrently. The proposed system has been implemented by constructing users' database patterns which are later matched to the biometric patterns entered by each user, thereby enabling the system to accept or reject the user. Experiments have been conducted to test the performance of the overall PBAS system and results obtained showed that this proposed system is reliable with many potential applications for computer security.Item Intelligent pressure-based typing biometrics system(Springer, Berlin, Heidelberg, 2004-09-20) Dahalan, Azweeda; Salami, Momoh-Jimoh E.; Lai, Weng K.; Ismail, Ahmad F.The design and development of a real-time enhanced password security system, based on the analysis of habitual typing rhythms of individuals, is discussed in this paper. The paper examines the use of force exerted on the keyboard and time latency between keystrokes to create typing patterns for individual users. Pressure signals which are taken from the sensors underneath the keypad are extracted accordingly. These are then used to recognize authentic users and reject imposters. An experimental setup has been developed to capture the pressure signal information of the users’ typing rhythm. Neuro-fuzzy system is employed as the classifier to measure the user’s typing pattern using the Adaptive Neural Fuzzy Inference System toolbox (ANFIS) in MATLAB.