Analysis of decision level fusion in multimodal biometrics using IRIS and fingerprint

2016 ◽  
Author(s):  
S.N. Garg ◽  
R. Vig ◽  
S. Gupta
Keyword(s):  
Multimodal Biometrics ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion

Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

2019 ◽  
Vol 9 (4) ◽  
pp. 1-14
Author(s):  
Priti Shivaji Sanjekar ◽  
Jayantrao B. Patil
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Wavelet Transformation ◽  
Multimodal Biometrics ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion ◽  
Fusion Approach ◽  
Match Score

Multimodal biometrics is the frontier to unimodal biometrics as it integrates the information obtained from multiple biometric sources at various fusion levels i.e. sensor level, feature extraction level, match score level, or decision level. In this article, fingerprint, palmprint, and iris are used for verification of an individual. The wavelet transformation is used to extract features from fingerprint, palmprint, and iris. Further the PCA is used for dimensionality reduction. The fusion of traits is employed at three levels: feature level; feature level combined with match score level; and feature level combined with decision level. The main objective of this research is to observe effect of combined fusion levels on verification of an individual. The performance of three cases of fusion is measured in terms of EER and represented with ROC. The experiments performed on 100 different subjects from publicly available databases demonstrate that combining feature level with match score level and feature level with decision level fusion both outperforms fusion at only a feature level.

scholarly journals Multimodal biometrics with serial, parallel and hierarchical mode at decision level fusion

2019 ◽  
Vol 16 (3) ◽  
pp. 1303
Author(s):  
Priti Shivaji Sanjekar ◽  
J. B. Patil
Keyword(s):  
Vital Role ◽  
Majority Voting ◽  
Multimodal Biometrics ◽  
Decision Level ◽  
Voting Rule ◽  
Mode Of Operation ◽  
Parallel Mode ◽  
Decision Level Fusion ◽  
Serial Mode ◽  
Level Fusion

<p>Biometric based personal authentication is playing a vital role in various security based applications. This paper presents the effective fusion of fingerprint, palmprint and iris traits at decision level. Combining different traits at the decision level is a challenging task due to less information available at this level. The focus of the work is to examine the performance of multimodal biometrics at decision level fusion in three different i.e. serial, parallel and hierarchical modes of operation. Serial mode is performed by taking unimodals serially while parallel mode of operation is carried out by processing all modals simulatenously using Majority Voting Rule and the hierarchical mode of operation is performed with proper combination of traits in parallel and serial mode using AND and OR rule. The experiments are performed on 100 different users from publically available FVC2006 fingerprint database, CASIA V1 palmprint database and IITD iris database. The experimental results suggest that proper fusion of different traits in hierarchical way can give best performance even at decision level fusion as compared to serial and parallel mode of operation.</p>

Hyperspectral remote sensing image classification based on decision level fusion

2011 ◽  
Vol 9 (3) ◽  
pp. 031002-31005 ◽  
Author(s):  
杜培军 Peijun Du ◽  
张伟 Wei Zhang ◽  
夏俊士 Junshi Xia
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Decision Level ◽  
Remote Sensing Image Classification ◽  
Decision Level Fusion ◽  
Hyperspectral Remote Sensing Image ◽  
Level Fusion

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

ODROID XU4 based implementation of decision level fusion approach for matching computer generated sketches

2016 ◽  
Vol 16 ◽  
pp. 217-224 ◽  
Author(s):  
Steven Lawrence Fernandes ◽  
G. Josemin Bala
Keyword(s):  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion ◽  
Fusion Approach
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