finger type classification from depth image

FINGER TYPE CLASSIFICATION FROM DEPTH IMAGE VERIFICATION

This kind of system gains attraction because the cost of biometric capture machines is low. However, with the current biometric technologies advancement, in parallel with the growth of threats in information security, biometric application systems have proliferated into the physical and logical access control domains. Law enforcement agencies were the first to adopt biometric systems in the 1970s to investigate criminals through fingerprint recognition. The results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification.īiometric via physiological attributes (fingerprint, dorsal vein, finger vein, and facial) or behavioral characters (signature, speech, and handwriting), refers to the science of validating and authenticating the identity of a person for automatic recognition. These showed that the accuracy of the proposed finger vein identification method is higher than the existing methods.

FINGER TYPE CLASSIFICATION FROM DEPTH IMAGE VERIFICATION

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. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. Each database was divided into training and testing sets.

These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Moreover, the feature extraction method, Hierarchical Centroid Feature Method (HCM), is fused with the statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. The image enhancement, Composite Median-Wiener (CMW) filter, improves image quality and preserves the edges. The objective of this paper is to address this issue by presenting two methods, a new image enhancement, and a feature extraction method. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. Several pieces of research have been carried out in this field but there is still an unresolved issue when data capturing and processing is of low quality. Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged due to residing underneath the skin.

Finger type classification from depth image