Biometric match score fusion using RVM: A case study in multi-unit iris recognition

2012 ◽  
Author(s):  
Hunny Mehrotra ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
Banshidhar Majhi
Keyword(s):  
Iris Recognition ◽  
Score Fusion ◽  
Match Score

INTEGRATING IMAGE QUALITY IN 2ν-SVM BIOMETRIC MATCH SCORE FUSION

2007 ◽  
Vol 17 (05) ◽  
pp. 343-351 ◽  
Author(s):  
MAYANK VATSA ◽  
RICHA SINGH ◽  
AFZEL NOORE
Keyword(s):  
Support Vector Machine ◽  
Iris Recognition ◽  
Quality Score ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Score Fusion ◽  
Redundant Discrete Wavelet Transform ◽  
Match Score

This paper proposes an intelligent 2ν-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2ν-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.

MATCH SCORE FUSION OF FINGERPRINT AND FACE BIOMETRICS FOR VERIFICATION

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Chiung Ching Ho ◽  
Mufaddal Ali Hussin ◽  
Hu Ng
Keyword(s):  
Feature Vector ◽  
Product Rule ◽  
New Approach ◽  
Biometric Systems ◽  
Score Fusion ◽  
Face Biometrics ◽  
Match Score

In recent years, attacks on password databases have been carried out at an increasing rate, with significant success. Thus, a new approach is needed to prove one's claim to identity instead of relying on a password. In this paper, we investigate the use of biometric match scores for the purpose of verification. Our work was performed using the BSSR1 multimodal match score biometric dataset, which contains match scores from face and fingerprint biometric systems. We investigated the use of match scores as a feature vector, and performed Simple Sum and Product Rule fusion of match scores. The results we obtained demonstrated that the use of match scores for verification purposes can be achieved to give a result that is highly accurate.

scholarly journals BIOMETRIC SCORE FUSION

2016 ◽  
Vol 2 (6) ◽  
Author(s):  
PANKAJ ,
Keyword(s):  
Frontal Area ◽  
Coarse Grained ◽  
Security Level ◽  
Focal Points ◽  
Biometric Data ◽  
Score Fusion ◽  
Relative Change ◽  
Modular Framework ◽  
Score Combination ◽  
Match Score

Multimodal biometric innovation in light of unique mark and finger knuckle has pulled in footing among scientists as of late. Despite the fact that Uni-modular framework offers many focal points, it has certain intrinsic shortcomings which deny it of the appeal. Uni-modular unique finger impression biometric frameworks performed singular acknowledgment in light of a particular wellspring of biometric data. However the match score esteem must be enhanced by working with low quality little closer view zone biometric pictures. In fact, the confirmation forms delivered by Finger Knuckle Print (FKP) brings about higher relative changes. The distortions between FKP pictures of same finger are of higher extent. The unimodal biometric check framework frequently gets influenced after accomplishing higher match score esteem. Besides, bimodal check framework does not accomplish higher security level which prompts to lesser combination score esteem. To diminish relative change on multimodal biometric framework, NonFracture based Fingerprint and Finger-Knuckle print Biometric Score Fusion (NFF-BSF) component is proposed in this paper. At first, particular estimation of match score is measured utilizing multimodal fitting coarse grained dissemination work. Multimodal fitting coarse grained dissemination capacity is utilized to work with low quality petite frontal area biometric pictures that accomplish high fitting score on the test and preparing biometric pictures. Also, Non-Fracture misshapening handling is completed in NFF-BSF instrument to diminish the adjustment fit as a fiddle of protest by utilizing bend length on biometric picture surfaces. At last, a coordinating technique in NFF-BSF instrument is utilized to decrease the relative changes. Thus, the relative changes on multimodal biometric framework expands the match score combination esteem. Investigation is directed on variables, for example, certifiable acknowledgment rate, coordinating score combination level and blunder rate on multimodal coordinating

Input images in iris recognition systems: A case study

2011 ◽  
Author(s):  
Inmaculada Tomeo-Reyes ◽  
Judith Liu-Jimenez ◽  
Ivan Rubio-Polo ◽  
Jorge Redondo-Justo ◽  
Raul Sanchez-Reillo
Keyword(s):  
Iris Recognition ◽  
Recognition Systems

scholarly journals Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Md. Rabiul Islam
Keyword(s):  
Likelihood Ratio ◽  
Iris Recognition ◽  
Feature Fusion ◽  
Ratio Score ◽  
Multimodal System ◽  
Classifier Selection ◽  
Multiple Classifier ◽  
Score Fusion ◽  
Fusion Approach ◽  
Voting Method

The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existingNhamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

Sign in / Sign up

Export Citation Format

Share Document