Deep hashing with multilayer CNN-based biometric authentication for identifying individuals in transportation security

Subba Reddy Borra (), B. Premalatha, G. Divya, B. Srinivasarao (), D. Eshwar, V. Bharath Simha Reddy () and Pala Mahesh Kumar ()
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Subba Reddy Borra: Malla Reddy Engineering College for Women
B. Premalatha: CMR College of Engineering and Technology
G. Divya: CMR Technical Campus
B. Srinivasarao: Koneru Lakshmaiah Education Foundation
D. Eshwar: Kommuri Pratap Reddy Institute of Technology
V. Bharath Simha Reddy: Malla Reddy Engineering College and Management Sciences
Pala Mahesh Kumar: SAK Informatics

Journal of Transportation Security, 2024, vol. 17, issue 1, No 3, 28 pages

Abstract: Abstract One of the biggest challenges in transportation security systems is ensuring reliable and effective identification of people. However, improved security in transportation systems was not achieved using the traditional biometric authentication methods. The objective of this research is to enhance the safety of transportation security systems through the deployment of a deep learning-based multimodal biometric authentication network (MMBA-Net). The network gathers biometric data, employs multilayer convolutional neural networks (ML-CNN) architecture to extract both low-level and high-level features, and extracts features using Deep Hashing Component Analysis (DHCA). An ML-CNN classifier is used to train the binary codes created from the retrieved features after they have been compressed. Large-scale dataset experiments demonstrate that DHCA performs better for accurate biometric authentication than state-of-the-art techniques.

Keywords: Biometric authentication; Transportation security; Deep hashing component analysis; Feature extraction; Multi-layer convolutional neural networks; Biometric data; Illumination variations (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12198-024-00272-w

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