Finger vein recognition based on an improved k-nearest centroid neighbor classifier
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Date
2017-06
Authors
Ng, Yee Wei
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Abstract
This project is developed to propose an improved K-Nearest Centroid Neighbor
classifier for finger vein recognition. Recently, finger vein recognition has become one
of the most popular biometric technologies to be used in various applications due to
finger vein‟s properties. Several classifiers have been proposed for the classification
process in finger vein recognition system. Compared to other classifiers, KNCN has
advantage of considering both proximity and spatial distribution. However, this
becomes a disadvantage as it may overestimate the range of NCN to be chosen. In
addition, in a typical KNCN classifier, the weightage of each nearest centroid neighbor
is not considered in the voting process. Besides, the classifier processing time increases
when a large value of k is chosen. Therefore, an improved KNCN classifier that
considers those problems is proposed for finger vein recognition in this project. This is
done by analyzing the typical KNCN classifier and applying modification on it to
improve its performance in term of accuracy and processing time. Based on a new NCN
selection method proposed, RSKNCN classifier had been proposed and had achieved
finger vein recognition rate of 87.64 % on FV-USM database which is 4.34 % higher
than the accuracy of a typical KNCN classifier. Modified version of RSKNCN classifier
had improved the processing time performance by achieving accuracy of 87.06 % with
182.94 ms/sample processing time performance. Although there is 0.58 % drop in
accuracy compared to RSKNCN classifier, the processing time performance had
shortened to 0.30 times of the processing time of RSKNCN classifier. Overall, this
project has successfully developed an improved KNCN classifier which achieved
balance performance between accuracy and processing time in finger vein recognition.