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

The k-nearest centroid neighbor kNCN classifier is one of the non-parametric classifiers which provide a powerful decision based on the geometrical surrounding neighborhood. Essentially, the main challenge in the kNCN is due to slow classification time that utilizing all training samples to find each nearest centroid neighbor. In this work, an adaptive k-nearest centroid neighbor (akNCN) is proposed as an improvement to the kNCN classifier. Two new rules are introduced to adaptively select the neighborhood size of the test sample. The neighborhood size for the test sample is changed through the following ways: 1) The neighborhood 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 neighbors if the maximum number of samples of the same class is found among jth nearest centroid neighbor. Thus, the size of neighborhood is adaptively changed to j. Experimental results on theFinger 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% whilemaintaining the classification accuracy

The Impact of Age on Human Face Matching Performance with Images of Children

This item is only available electronically.The ability to accurately conduct facial comparisons with images of children is instrumental for various applied purposes, such as the prevention of child trafficking. Despite this, previous research has shown that one-to-one face matching is especially challenging on images of young children and those which show significant age-related facial changes. However, limited research has tested performance on more operationally challenging face matching tasks (one-to-eight) using images of children. This study used a one-to-eight task to explore the extent that performance varied across three childhood age groups (0-5, 5-10 and 10-15) with a 5-year age variation between target and comparison images. Participants (N = 42) completed 120 randomised face matching trials and their accuracy and confidence ratings were analysed. Results found the worst performance for the 0-5-year age group (16% accuracy), compared to 5-10 (26%) and 10-15 (30%) groups, suggesting that performance increased with age. Additionally, no significant differences were found between target-present and target-absent trials. The alarmingly high error rates found in all conditions highlights the importance of understanding and improving performance. Future research should continue to build upon these findings by testing generalisability to practitioner populations, exploring individual differences and evaluating ways to improve performance.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 201