scholarly journals A robust person authentication system based on score level fusion of left and right irises and retinal features

2010 ◽  
Vol 2 ◽  
pp. 111-120 ◽  
Author(s):  
L. Latha ◽  
S. Thangasamy
Keyword(s):  
Score Level Fusion ◽  
Left And Right ◽  
Authentication System ◽  
Level Fusion

A Novel Biometric Authentication System with Score Level Fusion

2017 ◽  
Vol 4 (3) ◽  
pp. 383-404
Author(s):  
Ramesh Naidu Balaka ◽  
Prasad Babu Maddali Surendra
Keyword(s):  
Biometric Authentication ◽  
Score Level Fusion ◽  
Authentication System ◽  
Level Fusion

Fusion Time Reduction of a Feature Level Based Multimodal Biometric Authentication System

2020 ◽  
Vol 12 (1) ◽  
pp. 67-83
Author(s):  
Rasha O. Mahmoud ◽  
Mazen M. Selim ◽  
Omar A. Muhi
Keyword(s):  
Canonical Correlation ◽  
Recognition Accuracy ◽  
Biometric Authentication ◽  
Equal Error Rate ◽  
Histogram Of Oriented Gradient ◽  
The Face ◽  
Left And Right ◽  
Authentication System ◽  
Correlation Process ◽  
Level Fusion

In the present study, a multimodal biometric authentication method is presented to confirm the identity of a person based on his face and iris features. This method depends on multiple biometric techniques that combine face and iris (left and right) features to recognize. The authors have designed and applied a system to identify people. It depends on extracting the features of the face using Rectangle Histogram of Oriented Gradient (R-HOG). The study applies a feature-level fusion using a novel fusion method which employs both the canonical correlation process and the proposed serial concatenation. A deep belief network was used for the recognition process. The performance of the proposed systems was validated and evaluated through a set of experiments on SDUMLA-HMT database. The results were compared with others, and have shown that the fusion time has been reduced by about 34.5%. The proposed system has also succeeded in achieving a lower equal error rate (EER), and a recognition accuracy up to 99%.

scholarly journals Performance Evaluation of Multimodal Multifeature Authentication System UsingKNN Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gayathri Rajagopal ◽  
Ramamoorthy Palaniswamy
Keyword(s):  
Recognition Accuracy ◽  
Principal Component ◽  
Biometric System ◽  
Biometric Data ◽  
Score Level Fusion ◽  
Feature Level Fusion ◽  
Rich Information ◽  
Authentication System ◽  
Matching Score ◽  
Level Fusion

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

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