Feature-Level Fusion of Multimodal Biometric for Individual Identification by Training a Deep Neural Network

Nithya, B.; Sripriya, P.

doi:10.1007/978-981-16-5529-6_12

Cited by 1 publication

(1 citation statement)

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“…Similarly, it evades training a novel technique from scratch which requires more calculations and data. The authors in [26] presented complete detection accuracy without executing any preprocessing over the obtained images of fingerprint and face. To gain this, the features will be derived by utilizing HoG and Speeded up Robust Features (SURF) techniques.…”

Section: Related Workmentioning

confidence: 99%

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

Nair

Kumar³

2022

IJRITCC

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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.

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Section: Related Workmentioning

confidence: 99%