Published December 30, 2023 | Version CC BY-NC-ND 4.0
Journal article Open

Multimodal Biometrics for Human Identification using Artificial Intelligence

Creators

  • 1. Ph. D Scholar, UCE-Osmania University, Hyderabad (Telangana), India.

Contributors

Contact person:

  • 1. Ph. D Scholar, UCE-Osmania University, Hyderabad (Telangana), India.
  • 2. Department of Electronics & Communication Engineering, Stanley Engineering College (A), Hyderabad (Telangana), India.

Description

Abstract- Multimodal biometric systems combine multiple biometric modalities to enhance the accuracy and security of human identification. Instead of relying on a single biometric trait (such as fingerprint or face), these systems use a combination of different biometric characteristics to provide a more robust and reliable identification process. The key idea behind multimodal biometrics is that the fusion of diverse biometric data can overcome the limitations of individual modalities, resulting in higher accuracy and lower error rates.

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Additional details

Identifiers

DOI
10.35940/ijese.A4278.1212123
ISSN
2319-6378

Dates

Accepted
2023-12-15
Manuscript received on 09 August 2023 | Revised Manuscript received on 09 November 2023 | Manuscript Accepted on 15 December 2023 | Manuscript published on 30 December 2023

References

  • X. Chen, Z. Jing and G. Xiao, "Nonlinear fusion for face recognition using fuzzy integral", Communications in Nonlinear Science and Numerical Simulation, vol. 12, no. 5, 2007, pp. 823–831. https://doi.org/10.1016/j.cnsns.2005.07.005
  • M. Liu, X. Jiang and A. C. Kot, "Efficient fingerprint search based on database clustering", Pattern Recognition, vol. 40, no.6, 2007, pp. 1793-1803. https://doi.org/10.1016/j.patcog.2006.11.007
  • W. Xiong, K. A. Toh, W. Y. Yau and X. Jiang, "Model-guided deformable hand shape recognition without positioning aids", Pattern Recognition, vol. 38, no. 10, 2005, pp. 1651–1664. https://doi.org/10.1016/j.patcog.2004.07.008
  • W. W. Boles, "A security system based on human iris identification using wavelet transform", Engineering Applications of Artificial Intelligence, vol. 11, no. 1, 1998, pp. 77–85. https://doi.org/10.1016/S0952-1976(98)80006-7
  • D. A. van Leeuwen, A. F. Martin, M. A. Przybocki and J. S. Bouten, "NIST and NFI- TNO evaluations of automatic speaker recognition", Computer Speech & Language, vol. 20, no. 2-3, 2006, pp. 128-158. https://doi.org/10.1016/j.csl.2005.07.001
  • E. Yu and S. Cho, "Keystroke dynamics identity verification – its problems and practical solutions", Computers & Security, vol. 23, no. 5, 2004, pp. 428–440. https://doi.org/10.1016/j.cose.2004.02.004
  • L. Nanni, "An advanced multi-matcher method for on-line signature verification featuring global features and tokenised random numbers", Neurocomputing, vol. 69, no. 16–18, 2006, pp. 2402–2406. https://doi.org/10.1016/j.neucom.2006.02.009
  • R. Zhang, C. Vogler and D. Metaxas, "Human gait recognition at sagittal plane", Image and Vision Computing, vol. 25, no. 3, 2007, pp. 321–330. https://doi.org/10.1541/ieejeiss.126.1524
  • S. Ma, T. Zanma and M. Ishida, "Abstraction and implementation of human skill by hybrid dynamical system theory - application to an automatic driving system -", IEEJ Transactions on Electronics, Information and Systems, Vol. 126-C, No. 12, 2006, pp. 1524-1530. https://doi.org/10.1002/tee.20077
  • S. Ma, T. Zanma and M. Ishida, "Automatic Driving System Using Identification of Switched Systems with Unknown Switch Points", IEEJ Transactions on Electrical and Electronic Engineering, Vol. 1, No. 4, 2006, pp. 426-437.
  • J. Roll, A. Bemporad and L. Ljung, "Identification of piecewise affine system via mixed-integer programming", Automatica, vol. 40, no. 1, 2004.
  • Malshetty*, G., & Mathapati, Dr. B. (2019). WSN Clustering Based on EECI (Energy Efficient Clustering using Interconnection) Method. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 1, pp. 3564–3571). https://doi.org/10.35940/ijitee.a3799.119119
  • Maheswari, Dr. K. (2019). Finding Best Possible Number of Clusters using K-Means Algorithm. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1s4, pp. 533–538). https://doi.org/10.35940/ijeat.a1119.1291s419
  • B*, B. (2020). DHCFL: Dual Head Clustering Based On Fuzzy Logic for Wireless Sensor Networks. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 5, pp. 1049–1054). https://doi.org/10.35940/ijrte.e6279.018520
  • Alhassan, B. G., Yusof, F. B., & Norrulashikin, S. M. (2020). Assimilation of Principal Component Analysis and Wavelet with Kernel Support Vector Regression for Medium-Term Financial Time Series Forecasting. In International Journal of Management and Humanities (Vol. 4, Issue 7, pp. 40–48). https://doi.org/10.35940/ijmh.g0667.034720