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

Secured Online Payment Using Biometric Identification System

2013 ◽  
Vol 824 ◽  
pp. 193-199
Author(s):  
F.O. Agbontaen ◽  
P.E. Orukpe
Keyword(s):  
Smart Card ◽  
Biometric Identification ◽  
Identification System ◽  
Biometric System ◽  
Card Reader ◽  
Online Transactions ◽  
Online Identification ◽  
Online Payment ◽  
Smart Card Reader ◽  
Made In

In this paper, we will investigate the possibilities of using highly secured online payment via biometric system in the Nigerian environment. The system proposed enables the user to purchase any product online just by slotting a biometric embedded smart card in a portable handheld smart card reader that has a fingerprint scanner touch pad and can generate different code to authenticate online transactions. While online identification and verification processes are facing frauds and undesirable incidents, this biometric payment technology offers best solution. With the help of this technology, you can ensure that transactions on purchase of any product online are made in a reliable manner. Thus, no scope is left for criminal-minded people to create loopholes in e-commerce security.

scholarly journals Generalized net model of a biometric authentication system based on palm geometry and palm vein matching using intuitionistic fuzzy evaluations

2020 ◽  
Vol 26 (4) ◽  
pp. 71-79
Author(s):  
Zhelyana Ivanova ◽  
◽  
Veselina Bureva ◽  
Keyword(s):  
Access Control ◽  
Research Work ◽  
Biometric Authentication ◽  
Biometric System ◽  
The Real ◽  
Multimodal Biometric System ◽  
Intuitionistic Fuzzy ◽  
Authentication System ◽  
Generalized Net ◽  
Palm Vein

In the current research work a multimodal biometric system is investigated. It combines the palm vein authentication and palm geometry recognition methods. The system will be used to manage the access control. The apparatus of generalized nets is applied to model the biometric authentication processes. The constructed generalized net model of biometric authentication system based on palm geometry and palm vein matching using intuitionistic fuzzy evaluations can be used for simulation of the real processes. The intuitionistic fuzzy evaluations are used to compare the user traits with the templates stored in database.

scholarly journals Development of security systems using DNN and i & x-vector classifiers

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 32-45
Author(s):  
Orken Mamyrbayev ◽  
Aizat Kydyrbekova ◽  
Keylan Alimhan ◽  
Dina Oralbekova ◽  
Bagashar Zhumazhanov ◽  
...  
Keyword(s):  
Information Systems ◽  
Biometric Identification ◽  
Identification System ◽  
Security Systems ◽  
User Identification ◽  
Verification Algorithm ◽  
Voice Identification ◽  
Biometric Systems ◽  
Function Blocks ◽  
Individual Voice

The widespread use of biometric systems entails increased interest from cybercriminals aimed at developing attacks to crack them. Thus, the development of biometric identification systems must be carried out taking into account protection against these attacks. The development of new methods and algorithms for identification based on the presentation of randomly generated key features from the biometric base of user standards will help to minimize the disadvantages of the above methods of biometric identification of users. We present an implementation of a security system based on voice identification as an access control key and a verification algorithm developed using MATLAB function blocks that can authenticate a person's identity by his or her voice. Our research has shown an accuracy of 90 % for this user identification system for individual voice characteristics. It has been experimentally proven that traditional MFCCs using DNN and i and x-vector classifiers can achieve good results. The paper considers and analyzes the most well-known approaches from the literature to the problem of user identification by voice: dynamic programming methods, vector quantization, mixtures of Gaussian processes, hidden Markov model. The developed software package for biometric identification of users by voice and the method of forming the user's voice standards implemented in the complex allows reducing the number of errors in identifying users of information systems by voice by an average of 1.5 times. Our proposed system better defines voice recognition in terms of accuracy, security and complexity. The application of the results obtained will improve the security of the identification process in information systems from various attacks.

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 Robust Multimodal Biometric System VIA Multiple Svms

2019 ◽  
Vol 8 (11S) ◽  
pp. 203-209
Keyword(s):  
Support Vector ◽  
Biometric Authentication ◽  
Biometric System ◽  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Multimodal Biometric System ◽  
Average Accuracy ◽  
Vector Machines ◽  
Final Score ◽  
Matching Score

Generally single Support Vector Machine (SVM) is employed in existing multimodal biometric authentication techniques, and it assumes that whole set of the classifiers is available. But sometimes it is not possible due to some circumstances e.g. injury, some medical treatment etc. This paper includes a robust multimodal biometric authentication system that integrates FKP (Finger-Knuckle Print), face and fingerprint at matching score level fusion using multiple parallel Support Vector Machines (SVMs). Multiple SVMs are applied to overcome the problem of missing biometric modality. Every possible combination of three modalities (FKP, face and fingerprint) are taken into consideration and all combinations have a corresponding SVM to fuse the matching scores and produce the final score set for decision making. Proposed system is more flexible and robust as compared to existing multimodal biometric system with single SVM. The average accuracy of proposed system is estimated on a publicly available dataset with the use of MUBI tool(Multimodal Biometrics Integration tool) and MATLAB 2017b.

scholarly journals Hybridization for Classification and Identification of Individuals using Multimodal Biometric Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 2126-2132
Keyword(s):  
Real Life ◽  
Recognition System ◽  
Error Rates ◽  
Gabor Wavelet ◽  
Multimodal Fusion ◽  
Identification System ◽  
Biometric Recognition ◽  
Identification Rate ◽  
Biometric Systems ◽  
Biometric Characteristic

Biometric recognition systems use certain human characteristics such as voice, facial features, fingerprint, iris or hand geometry to identify an individual or verify their identity. These systems have been developed individually for each of these biometric modalities until they achieve remarkable levels of performance. Biometrics is a measure of biological characteristics for the identification or authentication of an individual based on some of its characteristics. Although biometric recognition techniques promise to be very effective, At present, we can not guarantee an excellent identification rate based on a single biometric signature with unimodal biometric systems. Thus the error rates of unimodal biometric systems are relatively high due to all these practical problems, which makes them impractical for the use of critical safety applications. To resolve these problems, a solution is used in the same system in several biometric modalities, called a multimodal biometric system (MBS). MBSs combine different modalities in a unique recognition system. The multimodal fusion allows improving the results obtained by a single biometric characteristic and making the system more robust to noise and interference and more resistant to possible attacks. Fusion may be carried out at the level of signals acquired by the different sensors, of the parameters obtained for each modality, of the scores provided by unimodal experts or of the decision taken by said experts. In the case of fusion, the features obtained from the various biometric methods must be homogenized before the process of fusion is accomplished. This article describes the evolution of a multi-modal biometric identification system depends on 3 biometrics-face, iris & fingerprint. Feature extraction is done using the Gabor Wavelet method and classification is accomplished using the Random Forest classifier. This proposed method is applicable in real-life applications to identify biometric for offices, hospitals, and colleges/universities and so on.

scholarly journals Impact on Biometric Identification Systems of COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuheng Guo
Keyword(s):  
Face Recognition ◽  
Comprehensive Evaluation ◽  
Biometric Identification ◽  
Biometric System ◽  
Biometric Systems ◽  
Face Detection And Recognition ◽  
Considerable Impact ◽  
Recognition Systems ◽  
The Impact ◽  
Detection And Recognition

COVID-19 has had an inevitable impact on the daily life of people in 2020. Changes in behavior such as wearing masks have a considerable impact on biometric systems, especially face recognition systems. When people are aware of this impact, a comprehensive evaluation of this phenomenon is lacking. The purpose of this paper is to qualitatively evaluate the impact of COVID-19 on various biometric systems and to quantitatively evaluate face detection and recognition. The experimental results show that a real-world masked face dataset is essential to build an effective face recognition-based biometric system.

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.

Non-Cooperative Facial Biometric Identification Systems

2011 ◽  
pp. 1259-1265
Author(s):  
Carlos M. Travieso González ◽  
Aythami Morales Moreno
Keyword(s):  
User Authentication ◽  
Biometric Identification ◽  
Identification System ◽  
Biometric Systems ◽  
Driver's License

The verification of identity is becoming a crucial factor in our hugely interconnected society. Questions such as “Is she really who she claims to be?”, “Is this person authorized to use this facility?” are routinely being posed in a variety of scenarios ranging from issuing a driver’s license to gaining entry into a country. The necessity for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometric systems, described as the science in order to recognize an individual based on his or her physical or behavioural traits, is beginning to get acceptance as a legitimate method in order to determine an individual’s identity. Nowadays, biometric systems have been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In particular, this work presents a non-cooperative identification system based on facial biometric.

Sign in / Sign up

Export Citation Format

Share Document