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Neighborhood Rough Set Approach With Biometric Application

Neighborhood Rough Set Approach With Biometric Application

B. Lavanya, Ahmad Taher Azar, H. Hannah Inbarani
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 20
ISSN: 1941-6253|EISSN: 1941-6261|EISBN13: 9781683180609|DOI: 10.4018/IJSKD.289041
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MLA

Lavanya, B., et al. "Neighborhood Rough Set Approach With Biometric Application." IJSKD vol.14, no.1 2022: pp.1-20. http://doi.org/10.4018/IJSKD.289041

APA

Lavanya, B., Azar, A. T., & Inbarani, H. H. (2022). Neighborhood Rough Set Approach With Biometric Application. International Journal of Sociotechnology and Knowledge Development (IJSKD), 14(1), 1-20. http://doi.org/10.4018/IJSKD.289041

Chicago

Lavanya, B., Ahmad Taher Azar, and H. Hannah Inbarani. "Neighborhood Rough Set Approach With Biometric Application," International Journal of Sociotechnology and Knowledge Development (IJSKD) 14, no.1: 1-20. http://doi.org/10.4018/IJSKD.289041

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Abstract

This paper provides a new approach for human identification based on Neighborhood Rough Set (NRS) algorithm with biometric application of ear recognition. The traditional rough set model can just be used to evaluate categorical features. The neighborhood model is used to evaluate both numerical and categorical features by assigning different thresholds for different classes of features. The feature vectors are obtained from ear image and ear matching process is performed. Actually, matching is a process of ear identification. The extracted features are matched with classes of ear images enrolled in the database. NRS algorithm is developed in this work for feature matching. A set of 20 persons are used for experimental analysis and each person is having six images. The experimental result illustrates the high accuracy of NRS approach when compared to other existing techniques.

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