Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

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Working process of MFFODL-MBV system
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Published: Dec 9, 2022
DOI: https://doi.org/10.17762/ijritcc.v10i1s.5798
Keywords:
Biometric verification; Multimodality; Security; Deep learning; Parameter tuning

Main Article Content

Venkata Ramana N.
Research scholar, Department of CSE, Annamalai University, Chidambaram, India
S. Anu H. Nair
Department of CSE, Annamalai University, Chidambaram, India (Deputed to WPT, Chennai)
K. P. Sanal Kumar
PG Department of Computer Science, R. V. Government Arts College, Chengalpattu, India

Abstract

Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models.

Article Details

How to Cite
Ramana N., V. ., Nair, S. A. H. ., & Sanal Kumar, K. P. . (2022). Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model. International Journal on Recent and Innovation Trends in Computing and Communication, 10(1s), 99–107. https://doi.org/10.17762/ijritcc.v10i1s.5798
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Vol. 10 No. 1s (2022): Special Issue on Enhancement and Innovations in Exploring Engineering
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Articles

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