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 2D Information Space Based Action Recognition

2019 ◽  
Vol 9 (1S) ◽  
pp. 295-298
Keyword(s):  
Convex Hull ◽  
Action Recognition ◽  
Video Sequence ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Human Action ◽  
New Approach ◽  
Human Object ◽  
Background Data ◽  
2D Data

Video based human action recognition has attained more attraction from the researchers and it predominates in the field of computer vision and pattern recognition. In this paper we deliver a new approach to suppress the background data and to extract 2D data of foreground human object of the video sequence. A combination of convex hull area, convex hull perimeter, solidity and eccentricity is used to represent the feature vector. Experiments are conducted on Weizmann video dataset to assess how the system is doing. The discriminative nature of the feature vectors assures accuracy in action recognition.

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

Score-Based Fusion in Multi-Unit Biometric Recognition System

2011 ◽  
Vol 48-49 ◽  
pp. 1010-1013 ◽  
Author(s):  
Yong Li ◽  
Jian Ping Yin ◽  
En Zhu
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Fusion Model ◽  
Biometric Recognition ◽  
Score Level Fusion ◽  
Fusion Rules ◽  
Biometric Systems ◽  
Score Fusion ◽  
Multiple Units ◽  
Level Fusion

The performance of biometric systems can be improved by combining multiple units through score level fusion. In this paper, different fusion rules based on match scores are comparatively studied for multi-unit fingerprint recognition. A novel fusion model for multi-unit system is presented first. Based on this model, we analyze the five common score fusion rules: sum, max, min, median and product. Further, we propose a new method: square. Note that the performance of these strategies can complement each other, we introduce the mixed rule: square-sum. We prove that square-sum rule outperforms square and sum rules. The experimental results show good performance of the proposed methods.

A NEW APPROACH FOR FACE-IRIS MULTIMODAL BIOMETRIC RECOGNITION USING SCORE FUSION

2013 ◽  
Vol 27 (03) ◽  
pp. 1356004 ◽  
Author(s):  
MARYAM ESKANDARI ◽  
ÖNSEN TOYGAR ◽  
HASAN DEMIREL
Keyword(s):  
Recognition Performance ◽  
Recognition System ◽  
Extraction Methods ◽  
Score Level Fusion ◽  
New Approach ◽  
Fusion Methods ◽  
Score Fusion ◽  
Multimodal Biometric Recognition ◽  
Matching Score ◽  
Level Fusion

In this paper, a new approach based on score level fusion is presented to obtain a robust recognition system by concatenating face and iris scores of several standard classifiers. The proposed method concatenates face and iris match scores instead of concatenating features as in feature-level fusion. The features from face and iris are extracted using local and global feature extraction methods such as PCA, subspace LDA, spPCA, mPCA and LBP. Transformation-based score fusion and classifier-based score fusion are then involved in the process to obtain, concatenate and classify the matching scores. Different fusion techniques at matching score level, feature level and decision level are compared with the proposed method to emphasize improvement and effectiveness of the proposed method. In order to validate the proposed scheme, a combined database is formed using ORL and BANCA face databases together with CASIA and UBIRIS iris databases. The results based on recognition performance and ROC analysis demonstrate that the proposed score level fusion achieves a significant improvement over unimodal methods and other multimodal face-iris fusion methods.

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