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Source publication
This paper presents a multi-instance fingerprint
authentication system, which is immune to various problems
encountered in unimodal systems. During enrolment, all available
fingerprints are captured and their unique features are extracted,
processed, and stored in a database alongside the bio-data of
individual subjects. Unique features of fingerpr...
Contexts in source publication
Context 1
... System automatically request for a random left hand fingerprint, at this time the left index is required to login while at later time or even to logout any of the remaining four may be demanded. After receiving a match (resulting from comparing between input fingerprints with the template) from the multi-instance trait requested to authenticate an individual, access into the door is granted ( figure 10) and denied otherwise as a result of a mismatch ( figure 11). Another important thing is that, the administrators can access time logs of both Login and Logout details. ...
Context 2
... System automatically request for a random left hand fingerprint, at this time the left index is required to login while at later time or even to logout any of the remaining four may be demanded. After receiving a match (resulting from comparing between input fingerprints with the template) from the multi-instance trait requested to authenticate an individual, access into the door is granted ( figure 10) and denied otherwise as a result of a mismatch ( figure 11). Another important thing is that, the administrators can access time logs of both Login and Logout details. ...
Context 3
... case of theft, there is a need to ascertain individuals' presence at a particular place per time; therefore narrows down the list of suspected individuals. All it requires is to simply check the logged-in ( figure 12) and out details (figure 13), the interval with respect to time places an individual at the crime scene in case of any. ...
Context 4
... system provides a flexible way of operations by allowing the administrator to choose a special login for the user who may at one point or the other suffer a wound or accidentally injure their fingerprint. In such cases, the administrator has the privilege of selecting the system mode of operation based on physical examination of available prints and setting the system to demand for such fingerprint as shown in figure 14. This makes the system to switch from demanding fingerprint from both hands to requesting exactly one print from a particular hand. ...
Context 5
... further emphasis the need for a flexible system and also man-machine synergy in technological advancement. Figure 15 shows at value 0.05 FAR and FRR is the same which is the equal error rate (EER); also at the minimum threshold the false acceptance rate is almost 100% and at maximum threshold the false rejection rate is almost 100% (including genuine) are rejected. With the equal error rate value at 0.05, so close to zero it implies that the system performance is good. ...
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Citations
... Multi-instance fusion would continue to be an active research area, according to a review of the multibiometric fusion strategy [10][11][12][13][14][15][16][17]6]. Therefore, this study proposed the use of GSA for fusion of texture features extracted from fingerprints in improving the performance of Multi-instance Fingerprint Authentication System (MFAS). ...
Multi-instance fusion of fingerprint authentication system at score level overcomes a few of the shortcomings of a Unimodal Biometric System (UBS) and enhanced the efficiency of the system. However, due to loss of information at higher levels, the features fused at the score level are confined in comparison to feature level fusion and could lead to poor performance. In this study, multi-instance fusion of fingerprints was done at feature level using Gravitational Search Algorithm (GSA) to select and combine minimal relevant informative texture features subsets from multi-instances of fingerprint and considerably improves the performance of the system. The approach was validated by creation of multi-instances of fingerprint database acquired locally from 150 subjects in an uncontrolled environment and texture based feature extraction was considered and classification of fused texture feature was done using back propagation neural network. The results show that the presented technique was effective in subject authentication with accuracy of 97.09%, indicating that it can successfully secure fingerprint authentication systems from unauthorized attacks.
... A multi-intance fingerprint based authentication system has been developed which consider more invincible to different problems encountered in previous systems using the crossing number technique [15]. The developed system considered highly efficient in verification the user with highly accuracy and low run time. ...
... The developed system considered highly efficient in verification the user with highly accuracy and low run time. Also, the system provides the flexibility to switch from multi-intance to unimodal in case of fault tolerance in order to preserve their independency [15]. ...
