Finger Vein Recognition Based On An Improved K-Nearest Centroid Neighbor Classifier - Ng, Yee Wei (Universiti Sains Malaysia, 2017)
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.