Multi-modal Bio-metrics Evaluation for Non-destructive Age States Determination of Tomato Plants (Solanum lycopersicum)

Every plant has unique morphological features, and can be used for its characteristics identity, such as age. When the plants grow, their morphological features may change, observable visually or by optical equipment. These various morphology transformations were categorized as multi-modal Bio-metrics. In this study, tomatoes from local cultivar were grown in a net house, in west Sumatra. The growth medium comprised of soil, husk, and manures with the composition of 1: 1: 1 respectively. For best growth, plants were watered regularly, and protect from pests and weeds. The observations were performed on 21st, 42nd, and 63rd day after sowing (DAS). The samples were the leaflets of the primary compound leaves of the plants. The leaflets were cut and digitized using a high-resolution colour scanner. The imaging performed at 300 dpi resolution, and the recorded image subsequently processed by the image processing software. Image segmentation performed to remove background from the object. Furthermore, the greenish of leaf object in the image were measured in RGB colour space. The leaf dimensions and area were quantified by the software, as well as the length of the leaflet main vein at central axis.  Two secondary leaflet’s blades were selected manually, and the angle formed between the blades and the main vein was measured. A Statistical engineering program was used to identify the principal morphology characteristics of the leaf, by means of Principal component analysis (PCA). Mathematical models were developed based on the principal component values and leaflets position to determine the plants age and state. Results showed all model have coefficient of correlation higher than 0.99 indicating acceptable accuracy

Improved methods for finger vein identification using composite median-wiener filter and hierarchical centroid features extraction

Finger vein identification is a potential new area in biometric systems. Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged because they reside underneath the skin of the finger and require a specific device to capture them. Research have been carried out in this field but there is still an unresolved issue related to low-quality data due to data capturing and processing. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. To address this issue, a new image enhancement and feature extraction methods were developed to improve finger vein identification. The image enhancement, Composite Median-Wiener (CMW) filter would improve image quality and preserve the edges of the finger vein image. Next, the feature extraction method, Hierarchical Centroid Feature Method (HCM) was fused with statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the one reported in the literature. As a conclusion, the results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification