Multimodal Biometric System and Information Fusion

2013 ◽  
pp. 48-68 ◽  
Keyword(s):  
Information Fusion ◽  
Fusion Method ◽  
Biometric System ◽  
Operational Parameters ◽  
Multimodal Biometric System ◽  
Biometric Systems ◽  
Fuzzy Fusion ◽  
Different Sources ◽  
Multimodal Biometric Systems

Integrating different information originating from different sources, known as information fusion, is one of the main factors of designing a biometric system involving more than one biometric source. In this chapter, various information fusion techniques in the context of multimodal biometric systems are discussed. Usually, the information in a multimodal biometric system can be combined in senor level, feature extraction level, match score level, rank level, and decision level. There is also another emerging fusion method, which is becoming popular—the fuzzy fusion. Fuzzy fusion deals with the quality of the inputs or with the quality of any system components. This chapter discusses the associated challenges related to making the choice of appropriate fusion method for the application domain, to balance between fully automated versus user defined operational parameters of the system and to take the decision on governing rules and weight assignment for fuzzy fusion.

scholarly journals A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques

2018 ◽  
Vol 7 (4.36) ◽  
pp. 689 ◽  
Author(s):  
A. S. Raju ◽  
V. Udayashankara
Keyword(s):  
Information Fusion ◽  
Human Identification ◽  
Future Research ◽  
Biometric System ◽  
Multimodal Biometrics ◽  
Multimodal Biometric System ◽  
Face Features ◽  
Biometric Systems ◽  
Characteristic Sequences ◽  
Multimodal Biometric Systems

Presently, a variety of biometric modalities are applied to perform human identification or user verification. Unimodal biometric systems (UBS) is a technique which guarantees authentication information by processing distinctive characteristic sequences and these are fetched out from individuals. However, the performance of unimodal biometric systems restricted in terms of susceptibility to spoof attacks, non-universality, large intra-user variations, and noise in sensed data. The Multimodal biometric systems defeat various limitations of unimodal biometric systems as the sources of different biometrics typically compensate for the inherent limitations of one another. The objective of this article is to analyze various methods of information fusion for biometrics, and summarize, to conclude with direction on future research proficiency in a multimodal biometric system using ECG, Fingerprint and Face features. This paper is furnished as a ready reckoner  for those researchers, who wish to persue their work in the area of biometrics.  

Fuzzy Fusion for Multimodal Biometric

2013 ◽  
pp. 98-111
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Mathematical Tool ◽  
Fusion Method ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Noisy Input ◽  
Fuzzy Fusion ◽  
Biometric Information ◽  
Fusion Approach

Fuzzy logic is a mathematical tool that can provide a simple way to derive a conclusion with the presence of noisy input information. It is a powerful intelligent tool and used heavily in many cognitive and decision-making systems. In this chapter, fuzzy logic-based fusion approach for multimodal biometric system is discussed. After discussing the basics of fuzzy logic, the fuzzy fusion mechanism in the context of a multimodal biometric system is illustrated. A brief discussion on the research conducted for fuzzy logic-based fusion in different application domains is also presented. The biggest advantage of the system is that instead of binary “Yes”/“No” decision, the probability of a match and confidence level can be obtained. A fuzzy fusion-based biometric system can be easily adjusted by controlling weight assignment and fuzzy rules to fit changing conditions. Some results of experimentations conducted in a recent research investigation on two virtual multimodal databases are presented. The discussion on the effect of incorporating soft biometric information with the fuzzy fusion method to make the system more accurate and robust is also included.

Fuzzy Fusion in Multimodal Biometric Systems

2007 ◽  
pp. 108-115 ◽  
Author(s):  
Vincenzo Conti ◽  
Giovanni Milici ◽  
Patrizia Ribino ◽  
Filippo Sorbello ◽  
Salvatore Vitabile
Keyword(s):  
Biometric Systems ◽  
Fuzzy Fusion ◽  
Multimodal Biometric Systems

Contemporary Biometric System Design

2020 ◽  
pp. 292-324
Author(s):  
Himanshu Purohit ◽  
Pawan K Ajmera
Keyword(s):  
Artificial Intelligence ◽  
Noisy Data ◽  
Error Rates ◽  
Biometric System ◽  
Behavioral Traits ◽  
Biometric Systems ◽  
Physical Traits ◽  
Multiple Scenarios ◽  
Different Levels ◽  
Multimodal Biometric Systems

Individual's Identity Authentication depends on physical traits like face, iris, and fingerprint, etc., or behavioral traits like voice and signature. With the rapid advancement in the field of biometrics, multimodal biometric systems are replacing unimodal biometric systems. As the application of molecular biometric system removes certain errors like noisy data, interclass variations, spoof attacks, and unacceptable error rates as compared to unimodal biometric systems. Even the possibilities of multiple scenarios present in multimodal biometric systems are quite helpful for the consolidation of information using different levels of fusion. In this chapter, the authors try to analyze the technological change which is present due to growing field of biometrics with artificial intelligence and undergone a thorough research for multimodal biometric systems for effective authentication purpose. This study is quite helpful for getting different perception for the use of biometrics as a highest level of network security due to the fusion of many different modalities.

scholarly journals Power Minimization Architecture for Multimodal Biometric System using Cadence

2020 ◽  
Vol 6 (9) ◽  
pp. 1-5
Keyword(s):  
Energy Efficiency ◽  
Low Power ◽  
Fir Filter ◽  
Behavioral Characteristics ◽  
Power Minimization ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Biometric Systems ◽  
Low Power Architectures

Physiological or behavioral characteristics of a person being identified or verified using biometric systems. The preprocessing block has the fir filter in which enhanced energy-efficiency has been obtained by introducing the low power architectures within it. The implementation of low power architectures in the fir filter part will further provide the optimization in the various parameters such as power, area and timing. Therefore, this will help us to do the biometrics process faster and efficient.

scholarly journals A Multimodal Biometric System using Iris and Palmprint

2020 ◽  
Vol 9 (3) ◽  
pp. 1892-1896
Keyword(s):  
Binary Code ◽  
Wavelet Packet ◽  
Current Approach ◽  
Weighted Sum ◽  
Fusion Method ◽  
Biometric System ◽  
Error Level ◽  
Multimodal Biometric System ◽  
Palm Print ◽  
Different Levels

A biometric system is basically a system of image recognition that uses bio metric characteristics to identify individuals. The thesis introduces a biometric multimodal system that is based on iris-based Palm Print verification and fusion. We suggest an approach to extracting features from each modality using four-level decomposition of the wavelet packet. It includes 256 packets capable of generating a simple binary code. Dictate standardized thresholds based on the first three highest energy peaks that would impact 0 or 1 for each wavelet packet. Specific fusion approaches were evaluated at different levels: character level, score level and error level. Its first fusion is an iris and palm print application, actually. For matching ratings the next one uses a weighted sum law. The next applies to the Hamacher t-norm's deficiencies. The standard database is used for testing the program proposed. The current approach and then each fusion method was checked for The consistency about the database of Casia iris merged with the database of Casia palm print. With each fusion process, the proposed solution to the multimodal biometric system achieves an increase in identification.

scholarly journals Multimodal Biometric Authentication for Smartphones

2021 ◽  
Vol 9 (12) ◽  
pp. 1559-1568
Author(s):  
Mrunal Pathak
Keyword(s):  
Multimodal Fusion ◽  
Biometric Identification ◽  
Identification System ◽  
Sensitive Information ◽  
Biometric Authentication ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Biometric Systems ◽  
Real Time Applications ◽  
Eye Recognition

Abstract: Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems

scholarly journals Research on Multi-element Fusion of Equipment Fault Monitoring Based on Evidence Theory

2020 ◽  
Vol 179 ◽  
pp. 02001
Author(s):  
Quan ZHOU ◽  
Lan LIU ◽  
Hao CHENG ◽  
Mian FU
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Health Monitoring ◽  
Evidence Theory ◽  
Reliable Data ◽  
Fusion Method ◽  
Relevant Evidence ◽  
Fault Monitoring ◽  
Different Sources

The multi-information fusion method of health monitoring based on evidence theory is used to study the problem of equipment fault diagnosis. The multi-information of fault monitoring is fused by the evidence theory and the reliability of the relevant evidence can be judged according to the ambiguity and uncertainty of the fault monitoring signal. Also it can determine the importance and reliability of the evidence from different sources. The data from multi-information fusion has higher reliability and accuracy which provides more reliable data for fault diagnosis.

scholarly journals A Robust Multimodel Biometric Crypto System

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1953-1961
Keyword(s):  
Final Decision ◽  
Key Generation ◽  
Biometric System ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Aes Algorithm ◽  
Behavioural Characteristics ◽  
Biometric Systems ◽  
Biometric Templates ◽  
Level Fusion

Biometric based authentication has several advantages over traditional password or PIN based authentication process because biometric is consists of physical or behavioural characteristics i.e fingerprint, face, Finger Knuckle Print (FKP), iris, voice etc. Unimodal biometric system h as some drawbacks i.e non universality, inter-class variation, intra-class variation; system can be circumvented by the skilled imposter etc. These drawbacks can overcome by multimodal biometric system as it combines more than one modality for authentication. When multimodal system combined with cryptography it makes system more robust and secure. In this paper, a robust multimodal biometric crypto system has been proposed, in which two modalities (FKP and face) are used for authentication of a person and one modality (fingerprint) is used for key generation. AES algorithm with fingerprint based key is used for securing the biometric templates. At authentication time, decision level fusion with AND rule is used for making the final decision. The proposed multimodal biometric crypto system is more robust and secure as compare with other multimodal biometric systems. Experimental results are shown with the help of MATLAB3. 2017b.

Sign in / Sign up

Export Citation Format

Share Document