Experimental Evaluation of Matching-Score Normalization Techniques on Different Multimodal Biometric Systems

2006 ◽  
Author(s):  
S. Ribaric ◽  
I. Fratric
Keyword(s):  
Experimental Evaluation ◽  
Biometric Systems ◽  
Score Normalization ◽  
Matching Score ◽  
Multimodal Biometric Systems

Score normalization in multimodal biometric systems

2005 ◽  
Vol 38 (12) ◽  
pp. 2270-2285 ◽  
Author(s):  
Anil Jain ◽  
Karthik Nandakumar ◽  
Arun Ross
Keyword(s):  
Biometric Systems ◽  
Score Normalization ◽  
Multimodal Biometric Systems

Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey)

2021 ◽  
Author(s):  
Mohamed Abdul-Al ◽  
George Kumi Kyeremeh ◽  
Naser Ojaroudi Parchin ◽  
Raed A Abd-Alhameed ◽  
Rami Qahwaji ◽  
...  
Keyword(s):  
Short Survey ◽  
Biometric Systems ◽  
Multimodal Biometric Systems

scholarly journals Multimodal Biometric Systems - Study to Improve Accuracy and Performance

2010 ◽  
Vol 1 (2) ◽  
pp. 54-61 ◽  
Author(s):  
K Sasidhar ◽  
Vijaya L Kakulapati ◽  
Kolikipogu Ramakrishna ◽  
K KailasaRao
Keyword(s):  
Biometric Systems ◽  
Improve Accuracy ◽  
And Performance ◽  
Multimodal Biometric Systems

Verification of Multimodal Biometric

2018 ◽  
pp. 147-193
Author(s):  
Shashidhara H. R. ◽  
Siddesh G. K.
Keyword(s):  
Multimodal Systems ◽  
Biometric Systems ◽  
Physical Traits ◽  
Reliability And Robustness ◽  
Secure Authentication ◽  
Physical Trait ◽  
Multimodal Biometric Systems

Authenticating the identity of an individual has become an important aspect of many organizations. The reasons being to secure authentication process, to perform automated attendance, or to provide bill payments. This need of providing automated authentication has led to concerns in the security and robustness of such biometric systems. Currently, many biometric systems that are organizations are unimodal, which means that use single physical trait to perform authentication. But, these unimodal systems suffer from many drawbacks. These drawbacks can be overcome by designing multimodal systems which use multiple physical traits to perform authentication. They increase reliability and robustness of the systems. In this chapter, analysis and comparison of multimodal biometric systems is proposed for three physical traits like iris, finger, and palm. All these traits are treated independently, and feature of these traits are extracted using two algorithms separately.

Beyond objective performance evaluation in multimodal biometric systems

2007 ◽  
Vol 62 (1-2) ◽  
pp. 156-176
Author(s):  
Doroteo T. Toledano ◽  
Álvaro Hernández Trapote ◽  
David Díaz Pardo de Vera ◽  
Rubén Fernández Pozo ◽  
Luis Hernández Gómez
Keyword(s):  
Performance Evaluation ◽  
Objective Performance ◽  
Biometric Systems ◽  
Multimodal Biometric Systems

CLASSIFIER COMBINATION AND ITS APPLICATION IN IRIS RECOGNITION

2008 ◽  
Vol 22 (03) ◽  
pp. 617-638 ◽  
Author(s):  
XINHUA FENG ◽  
XIAOQING DING ◽  
YOUSHOU WU ◽  
PATRICK S. P. WANG
Keyword(s):  
Iris Recognition ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition System ◽  
Classifier Combination ◽  
Practical Application ◽  
Finite Samples ◽  
Biometric Systems ◽  
Matching Score ◽  
Combined Classifier

Classifier combination is an effective method to improve the recognition accuracy of a biometric system. It has been applied to many practical biometric systems and achieved excellent performance. However, there is little literature involving theoretical analysis on the effectiveness of classifier combination. In this paper, we investigate classifiers combined with the max and min rules. In particular, we compute the recognition performance of each combined classifier, and illustrate the condition in which the combined classifier outperforms the original unimodal classifier. We focus our study on personal verification, where the input pattern is classified into one of two categories, the genuine or the impostor. For simplicity, we further assume that the matching score produced by the original classifier follows a normal distribution and the outputs of different classifiers are independent and identically distributed. Randomly-generated data are employed to test our conclusion. The influence of finite samples is explored at the same time. Moreover, an iris recognition system, which adopts multiple snapshots to identify a subject, is introduced as a practical application of the above discussions.

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