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Ensemble recognition model with optimal training for multimodal biometric authentication

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Abstract

Biometric security methods deploy any type of biometric recognition for safety purposes. Different from unimodal biometric system that uses single trait for security, the multimodal biometrical authentication (MBA) system deploys two or additional diverse biometric traits for authentication. In addition, MBA systems incurs high cost and have high level of security, as 2 biometric traits are complex for intruders to counterfeit more than a single trait. A new Ensemble Recognition Model with Optimal Training for Multimodal Biometric Authentication (ERMOTMBA) model is proposed in this work using Self Upgraded NBO (SU-NBO) algorithm. At first, median filtering is deployed to pre-process the palm print image, finger vein image, finger knuckle image, iris image and finger print image. The features extracted for palm print, finger vein, finger knuckle, iris and finger print are as follows: Palm print – extracting statistical features and line features, Finger vein – extracting bifurcation point and improved LGBP, Finger knuckle- Global feature and local feature, Iris- extracting 2D Gabor kernel and polynomial filtering, Finger print- Improved Minutiae feature (Binarization and thinning). After the extraction of feature set, enhanced feature level fusion (FLF) takes place under correlation basis. Finally, EC (with the combination of DBN, CNN and Bi-LSTM) is exploited, whose outputs are averaged to finalize the recognition outcome. Especially, proposing a training model for tuning the EC weights, and that is termed as SU-NBO algorithm.

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Data availability

The data is available in https://github.com/ujjwalll/GACMIS.

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Funding

This research did not receive any specific funding.

Author information

Authors and Affiliations

  1. PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India

    K. Pavan Kumar & Y. Suresh

  2. School of Computer Science and Engineering, NIST University, Berhampur, Orissa, India

    P. E. S. N. Krishna Prasad

  3. Department of Information Technology, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India

    M. Rajesh Babu

  4. Department of Computer Science and Engineering, Bapatla Engineering College, Bapatla, Andhra Pradesh, India

    M. Jogendra Kumar

  5. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India

    M. Jogendra Kumar

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  1. K. Pavan Kumar
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Correspondence to K. Pavan Kumar.

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Appendix

Appendix

1.1 Nomenclature

Abbreviation

Description

ACO

Ant Colony Optimization

DL

Deep Learning

DFS

Discriminative Factorized Subspaces

CBUA-OE

Continuous Biometric User Authentication, Online Evaluation

EVD

Eigen Value Decomposition

CNN

Convolutional Neural Network

Bi- LSTM

Bi-Directional Long Short Term Memory

DBN

Deep Belief Network

EC

Ensemble Classifier

FLF

Feature-Level Fusion

FV and FC

Fuzzy Vault and Fuzzy Commitment

FAR

False Acceptance Rate

FRR

False Rejection Rate

MAS

Multi Agent System

LBP

Local Binary Pattern

LFD

Local Feature Descriptors

LGBP

Local Gabor Binary Pattern

LoG

Laplacian of Gaussian

ML

Machine Learning

MWO

Modified Wolf Optimization

MF

Median Filtering

MBA

Multimodal Biometric Authentication

NBO

Namib Beetle Optimization

FC

Fully Connected

PSO

Particle Swarm Optimization

SD

Standard Deviation

SU-NBO

Self Upgraded NBO

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Kumar, K.P., Prasad, P.E.S.N.K., Suresh, Y. et al. Ensemble recognition model with optimal training for multimodal biometric authentication. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18541-0

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  • DOI: https://doi.org/10.1007/s11042-024-18541-0

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