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 Energy efficiency and I/O performance of low‐power architectures

2018 ◽  
Vol 31 (18) ◽  
Author(s):  
Pablo J. Pavan ◽  
Ricardo K. Lorenzoni ◽  
Vinícius R. Machado ◽  
Jean L. Bez ◽  
Edson L. Padoin ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Low Power ◽  
Low Power Architectures

Multimodal Biometric System

2011 ◽  
pp. 478-485 ◽  
Author(s):  
Ajita Rattani
Keyword(s):  
Access Control ◽  
Personal Identification ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Personal Authentication

Personal identification is a fundamental activity within our society. This identification is made possible by the emergence of the new concept of biometrics. Biometrics is the science of identifying or verifying an individual based on the physiological or behavioral characteristics like face, fingerprint, iris, signature, voice, retina, handwriting, and so forth. Biometric identifiers for personal authentication reduce or eliminate reliance on tokens, PINs, and passwords. It can be integrated into any application that requires security, access control, and identification or verification of people (Jain, Ross, & Prabhakar, 2004).

Overview of Biometrics and Biometrics Systems

2013 ◽  
pp. 6-25 ◽  
Keyword(s):  
System Performance ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Security Systems ◽  
Biometric Data ◽  
Multimodal Biometric System ◽  
System Components ◽  
Intelligent Information ◽  
And Performance ◽  
Intelligent Security

Recent security threats increase the necessity to establish the identity of every person. Biometric authentication is a solution to person authentication by analyzing physiological or behavioral characteristics. In this chapter, various biometric notions and terms are reviewed, along with typical biometric system components and different functionalities and performance parameters. The design and development of a biometric system, depending on a particular application scenario, is covered. This chapter also focuses on the inherent issues associated with biometric data and system performance through introducing radically new methods based on intelligent information fusion and intelligent pattern recognition, thus creating a notion of intelligent security systems. At the end of the chapter, the potential drawbacks of biometric unimodal systems, which serves as the motivation to introduce the concept of multimodal biometric system in the context of intelligent security systems, is discussed.

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

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.

Graphs in Biometrics

2011 ◽  
pp. 151-180
Author(s):  
Dakshina Ranjan Kisku ◽  
Phalguni Gupta ◽  
Jamuna Kanta Sing
Keyword(s):  
Real Time ◽  
Graph Matching ◽  
Image Plane ◽  
Individual Identification ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Biometric Systems ◽  
Pattern Recognition Systems ◽  
Recognition Systems ◽  
Matching Techniques

Biometric systems are considered as human pattern recognition systems that can be used for individual identification and verification. The decision on the authenticity is done with the help of some specific measurable physiological or behavioral characteristics possessed by the individuals. Robust architecture of any biometric system provides very good performance of the system against rotation, translation, scaling effect and deformation of the image on the image plane. Further, there is a need of development of real-time biometric system. There exist many graph matching techniques used to design robust and real-time biometrics systems. This chapter discusses different types of graph matching techniques that have been successfully used in different biometric traits.

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.

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.  

scholarly journals Biometric Technologies in Recognition Systems: A Survey

2019 ◽  
Vol 24 (6) ◽  
pp. 132
Author(s):  
Shihab A. Shawkat1 ◽  
Raya N. Ismail2
Keyword(s):  
Identity Management ◽  
Personal Attributes ◽  
Behavioral Characteristics ◽  
Human Society ◽  
Biometric System ◽  
Comprehensive Overview ◽  
Biometric Systems ◽  
Identity Management Systems ◽  
Recognition Systems ◽  
Hand Geometry

The ability to recognize people uniquely and to associate personal attributes such as name and nationality with them has been very important to the fabric of human society. Nowadays, modern societies have an explosion in population growth and increased mobility which necessitated building advanced identity management systems for recording and maintaining people’s identities. In the last decades, biometrics has played an important role in recognizing people instead of traditional ways such as passwords and keys which can be forgotten or be stolen. Biometric systems employ physiological and/or behavioral characteristics of people to verify their identities. There are different biometric modalities that can be used to recognize people such as fingerprints, face, hand geometry, voice, iris, signature, etc. In this paper, a comprehensive overview have been provided on the major issues of biometric systems including general biometric system architecture, major biometric traits, biometric systems performance, and some relevant works.   http://dx.doi.org/10.25130/tjps.24.2019.120

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