A methodology of nearest neighbor: Design and comparison of biometric image database

• 出版年: Proceedings - 14th IEEE Student Conference on Research and Development: Advancing Technology for Humanity, SCOReD 2016
• 第一著者: 2-s2.0-85014212341
• フォーマット: Conference paper
• 言語: English
• 出版事項: Institute of Electrical and Electronics Engineers Inc. 2017
• オンライン･アクセス: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014212341&doi=10.1109%2fSCORED.2016.7810073&partnerID=40&md5=3410f3616036a5b2a7ddfa4eb347c650

The nearest neighbor (NN) is a non-parametric classifier and has been widely used for pattern classification. Nevertheless, there are some problems encountered that leads to the poor performance of the NN i.e. the samples distribution, weighting issues and computational time for large databases. Hence, various classifiers i.e. k Nearest Neighbor (kNN), k Nearest Centroid Neighborhood (kNCN), Fuzzy k Nearest Neighbor (FkNN), Fuzzy-Based k Nearest Centroid Neighbor (FkNCN) and Improved Fuzzy-Based k Nearest Centroid Neighbor (IFkNCN) were proposed to improve the performance of the NN. This paper presents a review of aforementioned classifiers including the taxonomy, toward the implementation of classifiers in biometric image database. Two databases i.e. finger print and finger vein have been employed and the performance of classifiers were compared in term of processing time and classification accuracy. The results show that the IFkNCN classifier owns the best accuracies to the kNN, kNCN FkNN and FkNCN with 97.66% and 96.74% for fingerprint and finger vein databases, respectively. © 2016 IEEE.