A multimodal architecture using Adapt‐HKFCT segmentation and feature‐based chaos integrated deep neural networks (Chaos‐DNN‐SPOA) for contactless biometricpalm vein recognition system

Abstract

In the recent past, the fusion of various unimodal biometrics has gained increasing attention from researchers dedicated to the use of practical biometrics. In this paper, a Chaos Integrated Deep Neural Networks (Chaos-DNN) using Sandpiper Optimization Algorithm (SPOA) is proposed to enhance the perfor- | INTRODUCTIONThe biometric system is used to measure the signature points of the individual persons, such as behaviour of a person's identity. [1][2][3] Biometric systems are used for security purposes to control the data or information for future use. It is also used for internet access, securing computer systems, electronic passport control security, banking, cellular phones, credit cards, security access to buildings, health and social services, terrorist determination and corpse recognition. [4][5][6] The biometric technologies are developed using human traits, namely, DNA, ear morphology, facial characteristics, fingerprint, gait, hand geometry, finger geometry, iris, keystroke, smell, palm print, handwriting with signature, voice and so forth. Among them palm venal traits are gaining more attention and are comparatively accurate and secure. 7,8 Multimodal systems are widely used in defence to secure the data compared with the unimodal systems. The imagery of vein is caught in various portions of the hand, like, palm, palm dorsal, the wrist, finger and so forth, which are widely used in recent years of evaluation. 9,10 The vein images, like palm and dorsal part are helps to build low-cost devices. Also the image capture method is more user-friendly, less harm, rapid, lack of contact and easy to use more secure for authentication systems. Hand-based parameters are widely used because of ease to work, availability of various parameters, like, geometrical features, hand thickness, fingers, joints and RAJALAKSHMI AND ANNAPURANI PANAIYAPPAN | 1847 How to cite this article: Rajalakshmi M, Annapurani Panaiyappan K. A multimodal architecture using Adapt-HKFCT segmentation and feature-based chaos integrated deep neural networks (Chaos-DNN-SPOA) for contactless biometricpalm vein recognition system.

Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

“…A new Chaotic with SPO technique is named CSPO that changes the important parameter and embeds chaos as to recent SPO [19]. But, the SPO retains optimum convergence rate, it still could not implement appropriately in determining the global optimum that successively affects the convergence rate of technique.…”

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

“…al. ( 2020 ), Software Engineering (Amandeep et al 2020 ), Deep learning (Rajalakshmi and Annapurani Panaiyappan 2021 ), (Metan et al 2021 ). Kaur et.…”