An overview of hand-based multimodal biometrie system using multi-classifier score fusion with score normalization

Karthikeyan, S.; Mohamed, Ahmad Sufril Azlan; Ruhaiyem, Nur Intan Raihana

doi:10.1109/cspc.2017.8305806

Cited by 7 publications

(22 citation statements)

References 12 publications

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“…The proposed Hybrid BFO-PSO algorithm was a combination of BFO and PSO algorithms. It was proposed to mitigate individual weaknesses in the BFO and PSO algorithms which were slow convergence and premature convergence, respectively (Shanmugasundaram Mohamed, & Ruhaiyem, 2017b). There were three significant changes involved in the proposed hybrid BFO-PSO algorithm.…”

Section: Hybrid Improved Bacterial Swarm Optimization Algorithmmentioning

confidence: 99%

“…The BFO algorithm was affected by slow convergence. This was due to the fixed step size in the tumbling stage of the bacterium at the chemotaxis stage (Shanmugasundaram et al, 2017b). At the same time, however, it had the ability not to trap in local optima.…”

Section: Local Best By Bacterial Foraging Optimization Algorithmmentioning

confidence: 99%

“…Where ϴi(j+1,k,l) is the new position of the i th bacterium, ϴi(j,k,l) previous position of the i th bacterium, C(i)-step size , Øj -previous direction of the i th bacterium and Pbest is the local best of fitness value of ϴi(j+1,k,l) (Shanmugasundaram et al, 2017b).…”

Section: Local Best By Bacterial Foraging Optimization Algorithmmentioning

confidence: 99%

“…The bacterium in the BFO algorithm at the chemotaxis stage has two moves namely, swim and tumble. The swim is meant for moving the bacterium in the same direction whereas, the tumbling is meant for moving the bacterium in a random direction (Shanmugasundaram et al, 2017b).…”

Section: Adaptive Step Size In Tumbling Stage Of Bacterium Using Firementioning

confidence: 99%

“…So, it delays in reaching the global solution. To accelerate the bacterium movement, in the proposed HIBS (Shanmugasundaram et al, 2017b), the fixed step size was changed into varying step sizes ranging from [0,1] using the random walk procedure of the Firefly algorithm (Yang, 2009) to reach the optimum at the earliest convergence which is shown in Equation 3 and Equation 4.…”

Section: Adaptive Step Size In Tumbling Stage Of Bacterium Using Firementioning

confidence: 99%

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Hybrid Improved Bacterial Swarm Optimization Algorithm for Hand-Based Multimodal Biometric Authentication System

Karthikeyan

Mohmed

Ruhaiyem

2019

JICT

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This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively.

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Section: Hybrid Improved Bacterial Swarm Optimization Algorithmmentioning

confidence: 99%

Section: Local Best By Bacterial Foraging Optimization Algorithmmentioning

confidence: 99%

Section: Local Best By Bacterial Foraging Optimization Algorithmmentioning

confidence: 99%

Section: Adaptive Step Size In Tumbling Stage Of Bacterium Using Firementioning

confidence: 99%

Section: Adaptive Step Size In Tumbling Stage Of Bacterium Using Firementioning

confidence: 99%

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Hybrid Improved Bacterial Swarm Optimization Algorithm for Hand-Based Multimodal Biometric Authentication System

Karthikeyan

Mohmed

Ruhaiyem

2019

JICT

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Multispectral hand features for secure biometric authentication systems

Bartuzi

Trokielewicz

2021

Concurrency and Computation

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Summary With rapid growth of mobile‐based computing, reliable security and user authentication methods are necessary. This article proposes a proof‐of‐concept biometric authentication method utilizing hand images collected in different light spectra. An analysis of similarity between pattern of blood vessels extracted from near‐infrared images and thermal images and assessment of the correlation between individual biometric features contained in each image type is also performed. Results indicate a large potential of using thermal images in biometrics more extensively than now. Also proposed and evaluated are biometric recognition methods based on images of the hand acquired in visible light, near infrared, and using thermal infrared sensors. Two approaches were used to assess information content in images of each type: one based on texture descriptor and one employing convolutional neural networks. In an evaluation gathering data from 104 subjects, the former yielded the lowest equal error rate (EER) of 7.44%, whereas the latter approach gave EER=0.03% for thermal images. Finally, fusion of different‐spectra modalities increases accuracy and further reduces EER to 0.01%. This is, to the authors' best knowledge, the first study exploring the concept of fusing different spectral representations of the human hand for the purpose of biometric recognition.

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Design of Multimodal Biometric Information Management System Based on Commercial Systems

Zhu

Zhuang

Liu

et al. 2018

Biometric Recognition

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An overview of hand-based multimodal biometrie system using multi-classifier score fusion with score normalization

Cited by 7 publications

References 12 publications

Hybrid Improved Bacterial Swarm Optimization Algorithm for Hand-Based Multimodal Biometric Authentication System

Hybrid Improved Bacterial Swarm Optimization Algorithm for Hand-Based Multimodal Biometric Authentication System

Multispectral hand features for secure biometric authentication systems

Design of Multimodal Biometric Information Management System Based on Commercial Systems

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