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Information Resources Management Association
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Swarm Intelligence for Biometric Feature Optimization

Swarm Intelligence for Biometric Feature Optimization
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Author(s): Santosh Kumar (Indian Institute of Technology (Banaras Hindu University), India), Deepanwita Datta (Indian Institute of Technology (Banaras Hindu University), India) and Sanjay Kumar Singh (Indian Institute of Technology (Banaras Hindu University), India)
Copyright: 2017
Pages: 34
Source title: Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0788-8.ch032


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Swarm Intelligence (SI) and bio-inspired computation has gathered great attention in research in the last few years. Numerous SI-based optimization algorithms have gained huge popularity to solve the complex combinatorial optimization problems, non-linear design system optimization, and biometric features selection and optimization. These algorithms are inspired by nature. In biometrics, face recognition is a non-intrusive method, and facial characteristics are probably the most common biometric features to identify individuals and provide a competent level of security. This chapter presents a novel biometric feature selection algorithm based on swarm intelligence (i.e. Particle Swarm Optimization [PSO] and Bacterial Foraging Optimization Algorithm [BFOA] metaheuristics approaches). This chapter provides the stepping stone for future researchers to unveil how swarm intelligence algorithms can solve the complex optimization problems to improve the biometric identification accuracy. In addition, it can be utilized for many different areas of application.

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