Browsing by Author "Ali, Hasimah"

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    Iris recognition system by using support vector machines
    (IEEE, 2008-05-13) Ali, Hasimah; Salami, Momoh-Jimoh E.
    In recent years, with the increasing demands of security in our networked society, biometric systems for user verification are becoming more popular. Iris recognition system is a new technology for user verification. In this paper, the CASIA iris database is used for individual userpsilas verification by using support vector machines (SVMs) which based on the analysis of iris code as feature extraction is discussed. This feature is then used to recognize authentic users and to reject impostors. Support Vector Machines (SVMs) technique was used for the classification process. The proposed method is evaluated based upon False Rejection Rate (FRR) and False Acceptance Rate (FAR) and the experimental result show that this technique produces good performance.
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    Iris recognition system using support vector machines
    (IntechOpen, 2011-10-21) Ali, Hasimah; Salami, Momoh-Jimoh E.
    In the modern world, a reliable personal identification infrastructure is required to control the access in order to secure areas or materials. Conventional methods of recognizing the identity of a person by using passwords or cards are not altogether reliable, because they can be forgotten, stolen, disclosable, or transferable (Zhang, 2000). Biometric technology, which is based on physical and behavioral features of human body such as face, fingerprint, hand shapes, iris, palmprint, keystroke, signature and voice, (Lim et al., 2001, Zhang, 2000, Zhu et al., 1999) has now been considered as an alternative to existing systems in a great deal of application domains such as bank Automatic Teller Machines (ATM), telecommunication, internet security and airport security. Each biometric technology has its own advantages and disadvantages based on their usability and security. Among the various traits, iris recognition has attracted a lot of attention. Iris is an internal (yet externally visible) organ of the eye, which is well protected from the environment and its patterns are apparently stable throughout the life. The iris consists of variable sized hole called pupil. The average diameter of the iris is 12 mm, and the pupil size can vary from 10% to 80% of the iris diameter. It has the great mathematical advantage that its pattern variability amongst people is enormous (Daugman, 2002). The number of features in human iris is large. Its complex pattern can contain many distinctive features such as arching, ligaments, furrows, ridges, crypts, rings, corona, freckles and zigzag collarette (Wildes, 1999, Daugman, 2002) for personal identification. Fig. 1 is an example of human iris. That is because every iris has fine unique texture and does not change over time. In addition, iris pattern can have up to 249 independent degrees of freedom. Because of high randomness in the iris pattern, it has made the technique more robust and it is very difficult to deceive an iris pattern (Daugman, 2003). Unlike other biometric traits, iris recognition is the most accurate and non–invasive biometric for secure authentication and positive identification. This proposed system use a publicly availably library for iris recognition written in MATLAB (Masek, 2003). Due to the advantages of iris recognition systems which offer reliable and effective security in the present day, this research proposed the use of iris-based as verification system system to identify the person’s identity. This research work adopts Support Vector Machines (SVMs) as pattern classification techniques which are based on iris code model which the feature vector size is transformed to one-dimension vector which reduces to 1 x 480 by using averaging techniques (each segment is divided by 20) contains the average value torecognize an authorized user and unauthorized user. The effectiveness of the proposed system is evaluated based on False Rejection Rate (FRR) and False Acceptance Rate (FAR).
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    Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers
    (IEEE, 2009-03-06) Ali, Hasimah; Salami, Momoh-Jimoh E.
    Security of an information system depends to a large extent on its ability to authenticate legitimate users as well as to withstand attacks of various kinds. Confidence in its ability to provide adequate authentication is, however, waning. This is largely due to the wrongful use of passwords by many users. In this paper, the design and development of keystroke pressure-based typing biometrics for individual user's verification which based on the analysis of habitual typing of individuals is discussed. The paper examines the use of maximum pressure exerted on the keyboard and time latency between keystrokes as features to create typing patterns for individual users. Combining both an Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are adopted as classifiers to verify the authorized and unauthorized users based on extracted features of typing biometric. The effectiveness of the proposed system is evaluated based upon False Reject Rate (FRR) and False Accept Rate (FAR). A series of experiment shows that the proposed system that used combined classifiers produces promising result for both FAR and FRR.
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    Keystroke pressure-based typing biometrics authentication system using support vector machines
    (Springer, Berlin, Heidelberg, 2007-08-26) Martono, Wahyudi; Ali, Hasimah; Salami, Momoh-Jimoh E.
    Security of an information system depends to a large extent on its ability to authenticate legitimate users as well as to withstand attacks of various kinds. Confidence in its ability to provide adequate authentication is, however, waning. This is largely due to the wrongful use of passwords by many users. In this paper, the design and development of keystroke pressure-based typing biometrics for individual user’s verification which based on the analysis of habitual typing of individuals is discussed. The combination of maximum pressure exerted on the keyboard and time latency between keystrokes is used as features to create typing patterns for individual users so as to recognize authentic users and to reject impostors. Support vector machines (SVMs), which is relatively new machine learning, is used as a pattern matching method. The effectiveness of the proposed system is evaluated based upon False Reject Rate (FRR) and False Accept Rate (FAR). A series of experiment shows that the proposed system is effective for biometric-based security system.