Fuzzy Brain Storm Optimization and Adaptive Thresholding for Multimodal Vein-Based Recognition System

Verma, Dipti; Dubey, Sipi

doi:10.1142/s0218001417560079

Cited by 8 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Verma and Dubey 33 have presented a fuzzy brain storm optimization (FBSO) and an adaptive threshold for the multimodal vein-based recognition system. In this article, a multibiometric system with palm vein, hand vein and finger vein was proposed.…”

Section: Literature Surveymentioning

confidence: 99%

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

Rajalakshmi

Annapurani

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Surveymentioning

confidence: 99%

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

Rajalakshmi

Annapurani

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…Prolonged periods of surveillance can lead to a significant decline in human capabilities, while certain work environments may pose accessibility or safety challenges for individuals. Automated recognition systems have been developed for a variety of applications, due to the aforementioned reasons [1] . The practical applications of computer imitation of human vision have recently gained momentum due to the progress in computing capability and image processing technology.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of How Artificial Intelligence is Used in the Field of Image Identification

Mishra

2023

J. Res. Appl. Sci. Biotechnol.

View full text Add to dashboard Cite

Humans are adept at recognizing and detecting items that are right in front of them. People are extremely aware of how quickly and accurately the human visual system can carry out difficult tasks like object recognition and identification. But imagine a scenario in which they must retrieve a ring from a table that contains various-sized boxes and other objects. It will take a while to look for the key, and they will encounter several challenges. With the help of a computer program, one can quickly locate a ring, and with the help of a large quantity of data and an algorithm, one can quickly train datasets to accurately recognize and categorize a variety of items. Machine learning (ML) and artificial intelligence (AI) are current trends. The most well-known area of artificial intelligence is computer vision. Computer science and software that can detect and comprehend pictures are known as "computer vision." It also has object detection, imagine recognition, and more. Author of the paper has tried to describe the ideas behind contemporary object detection, object categorization, and object recognition.

show abstract

“…Therefore, many heuristic or evolutionary optimization algorithms were developed for global optimization problems [8,12,27]. Through modeling human's brainstorming process, the Brain Storm Optimization (BSO) algorithm proposed recently in [19,20] has become a promising population-based evolutionary algorithm, and has attracted more and more theoretical analysis [3,4,9,11,21,22,31] and practical applications [5,14,17,24,25]. An important progress of BSO is to transform operations in the solution space to the objective space [22].…”

Section: Introductionmentioning

confidence: 99%

Simplex Search Based Brain Storm Optimization

Chen¹,

Cao²,

Cheng³

et al. 2017

Preprint

View full text Add to dashboard Cite

Through modeling human's brainstorming process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm. However, BSO is pointed out that it possesses a degenerated L-curve phenomenon, i.e., it often gets near optimum quickly but needs much more cost to improve the accuracy. To overcome this question in this paper, an excellent direct search based local solver, the Nelder-Mead Simplex (NMS) method is adopted in BSO. Through combining BSO's exploration ability and NMS's exploitation ability together, a simplex search based BSO (Simplex-BSO) is developed via a better balance between global exploration and local exploitation. Simplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions, and alleviate significantly this phenomenon on multimodal functions. Large number of experimental results show that Simplex-BSO is a promising algorithm for global optimization problems.

show abstract

Fuzzy Brain Storm Optimization and Adaptive Thresholding for Multimodal Vein-Based Recognition System

Cited by 8 publications

References 25 publications

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

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

An Analysis of How Artificial Intelligence is Used in the Field of Image Identification

Simplex Search Based Brain Storm Optimization

Contact Info

Product

Resources

About