Hardware core of pipelined thinning algorithm

The process to associate a particular individual with an identity is known as personal recognition. One of the recognition system type is biometrics system. A biometric system is essentially a pattern recognition system that that performs authentication based on the individual’s behavioural or physiological characteristics. One of the method is finger vein biometrics, which is an authentication technique that identify individuals and verify identity based on the images of human finger veins beneath the skin. There are a lot of process involved in a complete biometrics system. One of the process is thinning. Thinning or skeletonization is a process that extracts the vein patterns from binary image and produces 1-pixel wide output binary image as the result. Existing biometrics system have their own weaknesses and drawbacks such as not showing "aliveness" and also easy to be tampered with. Moreover, software implementation of biometrics system usually performed in an insecure environment and biometrics template stored in a central server. This is insecure and can cause leakage of information. Furthermore, thinning is a time consuming process, which takes a very long time to be completed in software implementation. So the objective of this work is to design a dedicated hardware core for thinning algorithm, implement and enhance the existing algorithm for better hardware performance, and apply pipeline architecture to the hardware design to further speed-up thinning process. This work will implement the algorithm in software and hardware. Hardware implementation of the algorithm is compared with software implementation in terms of accuracy and performance (speed). The hardware core designed managed to achieve a significant improvement in processing time. The work done in this project also managed to map a complex algorithm into hardware implementation and is the first one to implement thinning hardware design using System Verilog

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition

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

Profundización en el reconocimiento biométrico mediante venas del dedo

El uso de los patrones de las venas de los dedos de la mano como nueva forma de reconocimiento biométrico es una idea que está emergiendo. Este nuevo tipo de biometría ya tiene dispositivos comerciales pero la investigación científica solo está comenzando. El propósito de estas investigaciones es cubrir dicha brecha entre la investigación y los dispositivos comerciales. El trabajo actual se ha realizado empleando una base de datos que nos ha proporcionado un grupo de investigación de la University of Twente [1] (Holanda), con el objetivo de comparar los resultados con los que se obtuvieron sobre nuestros propios datos, recogidos durante el curso 2015/2016. Se han aplicado técnicas de reescalado y preprocesado a las imágenes de los dedos de la mano lo que permite resaltar el patrón de venas del dedo. Finalmente, se ha construido un sistema de verificación biométrica basado en esas imágenes mejoradas. Para ello, se han probado varios sistemas con clasificadores basados en distancias: uno basado en correlación de imágenes (será nuestro sistema de referencia), otro basado en la transformada discreta del coseno y un tercero basado en binarización que utiliza la distancia de Hamming. Los dos últimos son una propuesta bastante novedosa para este tipo de investigación.Grado en Ingeniería Informátic