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Biometric Identification System Using Neuro and Fuzzy Computational Approaches

Biometric Identification System Using Neuro and Fuzzy Computational Approaches

Tripti Rani Borah, Kandarpa Kumar Sarma, Pranhari Talukdar
Copyright: © 2016 |Pages: 36
ISBN13: 9781522501596|ISBN10: 1522501592|EISBN13: 9781522501602
DOI: 10.4018/978-1-5225-0159-6.ch027
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MLA

Borah, Tripti Rani, et al. "Biometric Identification System Using Neuro and Fuzzy Computational Approaches." Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 634-669. https://doi.org/10.4018/978-1-5225-0159-6.ch027

APA

Borah, T. R., Sarma, K. K., & Talukdar, P. (2016). Biometric Identification System Using Neuro and Fuzzy Computational Approaches. In I. Management Association (Ed.), Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications (pp. 634-669). IGI Global. https://doi.org/10.4018/978-1-5225-0159-6.ch027

Chicago

Borah, Tripti Rani, Kandarpa Kumar Sarma, and Pranhari Talukdar. "Biometric Identification System Using Neuro and Fuzzy Computational Approaches." In Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 634-669. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0159-6.ch027

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Abstract

In all authentication systems, biometric samples are regarded to be the most reliable one. Biometric samples like fingerprint, retina etc. is unique. Most commonly available biometric system prefers these samples as reliable inputs. In a biometric authentication system, the design of decision support system is critical and it determines success or failure. Here, we propose such a system based on neuro and fuzzy system. Neuro systems formulated using Artificial Neural Network learn from numeric data while fuzzy based approaches can track finite variations in the environment. Thus NFS systems formed using ANN and fuzzy system demonstrate adaptive, numeric and qualitative processing based learning. These attributes have motivated the formulation of an adaptive neuro fuzzy inference system which is used as a DSS of a biometric authenticable system. The experimental results show that the system is reliable and can be considered to be a part of an actual design.

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