Finger Vein Recognition Using Principle Component Analysis and Adaptive k-Nearest Centroid Neighbour Classifier

The k-nearest centroid neighbour kNCN classifier is one of the non-parametric classifiers which provide a powerful decision based on the geometrical surrounding neighbourhood. Essentially, the main challenge in the kNCN is due to slow classification time that utilizing all training samples to find each nearest centroid neighbour. In this work, an adaptive k-nearest centroid neighbour (akNCN) is proposed as an improvement to the kNCN classifier. Two new rules are introduced to adaptively select the neighbourhood size of the test sample. The neighbourhood size for the test sample is changed through the following ways: 1) The neighbourhood size, k will be adapted to j if the centroid distance of j-th nearest centroid neighbor is greater than the predefined boundary. 2) There is no need to look for further nearest centroid neighbours if the maximum number of samples of the same class is found among j-th nearest centroid neighbour. Thus, the size of neighbourhood is adaptively changed to j. Experimental results on the Finger Vein USM (FV-USM) image database demonstrate the promising results in which the classification time of the akNCN classifier is significantly reduced to 51.56% in comparison to the closest competitors, kNCN and limited-kNCN. It also outperforms its competitors by achieving the best reduction ratio of 12.92% while maintaining the classification accuracy.