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Cryptography and Tay-Grey wolf optimization based multimodal biometrics for effective security

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Abstract

Biometric recognition is very important for automatically recognizing individuals based on feature vectors from behavioral or physiological characteristics. The biometric recognition systems provide suitable personal recognition approaches for determining individuals. Biometrics are broadly employed in several commercial as well as official identification systems for automatic access control. This paper introduces the model for multi-modal biometric recognition based on the feature level fusion method. The overall procedure of the proposed method involves four steps: pre-processing, feature extraction, recognition feature-level fusion, and Bio-metric recognition. The first step is to input the images into pre-processing steps. Thus, pre-processing three traits, like face, finger knuckle, and the hand vein, is done. Then, the feature extraction is done for each modality to extract the features. After that, the feature level fusion is carried out using Elliptic-curve cryptography (ECC) and the proposed Taylor-Grey Wolf optimization (Tay-GWO). After feature fusion, the Bio-metric recognition is done based on Deep Convolutional Neural Network (DCNN), which Tay-GWO trains. The proposed Tay-GWO is designed by integrating the Taylor series and Grey Wolf Optimization (GWO). The analysis shows that the developed model achieves the maximal accuracy of 94.86%, maximal sensitivity of 96.80%, and specificity of 93.74%, respectively.

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Authors and Affiliations

  1. Dr. C V Raman Institute of Science and Technology, Kargi Road, Kota, Chhattisgarh, 495113, India

    Ankit Arora

  2. Dr. C V Raman Institute of Science and Engineering, Kargi Road, Kota, Chhattisgarh, 495113, India

    Rohit Miri

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  1. Ankit Arora
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  2. Rohit Miri
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Correspondence to Ankit Arora.

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Arora, A., Miri, R. Cryptography and Tay-Grey wolf optimization based multimodal biometrics for effective security. Multimed Tools Appl 81, 44021–44043 (2022). https://doi.org/10.1007/s11042-022-11993-2

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  • DOI: https://doi.org/10.1007/s11042-022-11993-2

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