MULTIMODAL BIOMETRIC SYSTEMS: A REVIEW

Article Sidebar

PDF
Published: Apr 20, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i2.5742
Keywords:
Unimodal biometrics, Multimodal Biometrics, Feature Vector, Fusion level, Score level, Decision Level

Main Article Content

Aarvi Thadeshwar
NMIMS' Mukesh Patel School of Technology Management and Engineering
Sanyamee Patel
NMIMS' Mukesh Patel School of Technology Management and Engineering
Dhruvi Patel
NMIMS' Mukesh Patel School of Technology Management and Engineering
Ratnesh Chaturvedi
NMIMS' Mukesh Patel School of Technology Management and Engineering

Abstract

The need for biometrics is rising with an increased need for security in every organization of the world. Every attribute, fingerprint, iris, knuckle print, face, palm print, is unique to an individual and can aid in recognition. Unimodal biometric systems involve the use of a single biometric attribute for identification. However, with the rise of duping, there is a requirement for constructing an efficient system using multiple biometric attributes. In multimodal systems, fusion of various biometric identities into a single vector is done using algorithms. This paper covers several different techniques used for biometric authentication. These use varying numbers of attributes. Our future project focuses on the research of different techniques of multimodal recognition in order to devise a technique that is robust and has an increased efficiency in recognition and identification.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 9 No. 2 (2018): March-April 2018
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.
Author Biographies

Aarvi Thadeshwar, NMIMS' Mukesh Patel School of Technology Management and Engineering

Student, B. Tech Computer Engineering

Sanyamee Patel, NMIMS' Mukesh Patel School of Technology Management and Engineering

Student, B. Tech Computer Engineering

Dhruvi Patel, NMIMS' Mukesh Patel School of Technology Management and Engineering

Student, B. Tech Computer Engineering

Ratnesh Chaturvedi, NMIMS' Mukesh Patel School of Technology Management and Engineering

Assistant Professor, Computer Engineering Department

References

Oloyede, Muhtahir O., and Gerhard P. Hancke. â€Unimodal and mul- timodal biometric sensing systems: a review.†IEEE Access 4 (2016): 7532-7555.

Thepade, Sudeep D., and Rupali K. Bhondave. â€Bimodal biometric identification with Palmprint and Iris traits using fractional coefficients of Walsh, Haar and Kekre transforms.†In Communication, Information Computing Technology (ICCICT), 2015 International Conference on, pp. 1-4. IEEE, 2015.

Geetha, K., and V. Radhakrishnan. â€Multimodal Biometric System: A Feature Level Fusion Approach.†International Journal of Computer Applications 71, no. 4 (2013).

Thepade, Sudeep D., Rupali K. Bhondave, and Ashish Mishra. â€Compar- ing Score Level and Feature Level Fusion in Multimodal Biometric Iden- tification Using Iris and Palmprint Traits with Fractional Transformed Energy Content.†In Computational Intelligence and Communication Networks (CICN), 2015 International Conference on, pp. 306-311. IEEE, 2015.

Meraoumia, Abdallah, Salim Chitroub, and Ahmed Bouridane. â€Robust multimodal biometric identification system using Finger-Knuckle-Print features.†In Control, Engineering Information Technology (CEIT), 2015 3rd International Conference on, pp. 1-6. IEEE, 2015.

Thepade, Sudeep D., and Rupali K. Bhondave. â€Multimodal identifi- cation technique using Iris Palmprint traits with matching score level in various Color Spaces with BTC of bit plane slices.†In Industrial Instrumentation and Control (ICIC), 2015 International Conference on, pp. 1469-1473. IEEE, 2015.

Aizi, Kamel, Mohamed Ouslim, and Ahmed Sabri. â€Remote multi- modal biometric identification based on the fusion of the iris and the fingerprint.†In Electrical Engineering (ICEE), 2015 4th International Conference on, pp. 1-6. IEEE, 2015.

ARAVALLI, NAGAMMA. â€Automatic System for Person Authentica- tion by Multimodal Biometrics- A Survey.â€

Jain, Anil K., Lin Hong, and Yatin Kulkarni. â€A multimodal biometric system using fingerprint, face and speech.†In Proceedings of 2nd Int’l Conference on Audio-and Video-based Biometric Person Authentication, Washington DC, pp. 182-187. 1999.

Madane, Manisha, and Sudeep Thepade. â€Score Level Fusion Based Bimodal Biometric Identification Using Thepade’s Sorted n-ary Block Truncation Coding with Variod Proportions of Iris and Palmprint Traits.†Procedia Computer Science 79 (2016): 466-473.