An Efficient Vein Pattern-based Recognition System

This paper presents an efficient human recognition system based on vein pattern from the palma dorsa. A new absorption based technique has been proposed to collect good quality images with the help of a low cost camera and light source. The system automatically detects the region of interest from the image and does the necessary preprocessing to extract features. A Euclidean Distance based matching technique has been used for making the decision. It has been tested on a data set of 1750 image samples collected from 341 individuals. The accuracy of the verification system is found to be 99.26% with false rejection rate (FRR) of 0.03%.Comment: IEEE Publication format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Gait Verification using Knee Acceleration Signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively

An Analytical Survey on Vein Pattern Recognition

Biometric is term of science to identify a person identity using their physiological features. Currently, vein pattern recognition has attracted the attention of the technology and industry all over the world. A vein is network of blood vessels under the skin of an individual. The vascular pattern is different for every person in the same part or region of the body. It is stable till very long age. As the veins are underneath the skin it is very difficult for intruder or forger to copy the feature. This uniqueness and strong immunity from intruders make it more potent biometric system which avails us secure features for individual identity verification. This paper involves the description of vein pattern recognition, its requirement and its importance in biometric system. Different feature extraction algorithms are reviewed as independent component analysis, principal component analysis method. For classification in vein pattern recognition we have reviewed support vector machine and neural network techniques. Parameters are described based on which results are computed like true positive, false positive, true negative, false negative, accuracy and precision

Pré-Processamento e Classificação de Imagens NIR das Veias da Palma da Mão e Pulso Utilizando Análise por Componentes Principais

O objetivo deste trabalho é pré-processar imagens das palmas da mãos e punhos de indivíduos no intuito de aprimorar a acurácia de um modelo classificador. Tal processamento é feito para evidenciar as veias nas imagens como fator a ser usado para o reconhecimento do indivíduo. A classificação é feita empregando a análise por componentes principais, a partir de um banco de imagens no espectro do infravermelho, contendo 2400 imagens de 50 indivíduos diferentes. Os resultados obtidos utilizando o pré-processamento proposto mostram uma melhora da acurácia média de 18% em relação ao modelo que utilizou as imagens originais

Hand Vein Pattern Recognition using Natural Image Statistics

Biometrics is the science of identifying a person using physiological or behavioural characteristics. Hand vein pattern is a recent and unique biometric feature which is used for high secure authentication of individuals. The dorsal hand contains dorsal metacarpal veins, dorsal venous network, cephalic vein and basilic vein.  This paper presents an image descriptor which uses statistical structure of natural images. In this work, stack of natural image patches are used as filters and these transform an image into integer labels describing the small-scale appearance of the image. These labels are converted into histogram and it is used for further image analysis. The feature space contains binarized statistical image features. The proposed work is tested on NCUT dataset with state-of-the-art algorithms. The experimental results demonstrate that the proposed work outperforms of the state-of-the-art algorithms with the recognition rate of 99.80 per cent.Defence Science Journal, Vol. 65, No. 2, March 2015, pp.150-158, DOI:http://dx.doi.org/10.14429/dsj.65.731

Local Descriptor Approach to Wrist Vein Recognition with DVH-LBP Domain Feature Selection Scheme

Local Binary Pattern (LBP) is one of the well-known image recognition descriptors for texture-based images due to its superiority. LBP can represent texture well due to its ability to discriminate and compute efficiency. However, when it is used to describe textures that are barely visible, such as vein images (especially contactless vein), its discrimination ability is reduced, which leads to lower performance. LBP has extensively been implemented for features extraction in recognition system of hand, eye, face, eye, and other images. Nowadays, there are a lot of developments of hand recognition systems as a hand is a part of the body that can be easily used in the recognition process and it is easier to contact the sensor when taking the image (user-friendly). In particular, a hand consists of various parts that can be used, such as palm and fingers. Other parts like dorsal and wrist can also be used as they have unique characteristics, i.e., they are different from each other, and they do not change with ages. Changes in pixel intensity can be derived from skeletal vein images to distinguish individuals in palm vein recognition. In the previous paper, we proposed a method diagonal, vertical, horizontal local binary pattern (DVH-LBP) for implementing the palm vein recognition system successfully. Through this work, we improve our previous procedure and implement the improved method for recognizing wrist. In particular, this study proposes a new and robust directional extraction technique for encoding the functions of the wrist vein in a simple representation of binary numbers. Simulation results show the low equal error rate (ERR) of the proposed technique is 0.012, and the recognition rate is 99.4%

Electronic speckle pattern interferometry for fracture expansion in nuclear graphite based on PDE image processing methods

Session PS - Posters: Paper 9525-103Conference 9525Nuclear graphite has been widely used as moderating and reflecting materials. However, due to severe neutron irradiation under high temperature, nuclear graphite is prone to deteriorate, resulting in massive microscopic flaws and even cracks under large stress in the later period of its service life. It is indispensable, therefore, to understand the fracture behavior of nuclear graphite to provide reference to structural integrity and safety analysis of nuclear graphite members in reactors. In this paper, we investigated the fracture expansion in nuclear graphite based on PDE image processing methods. We used the second-order oriented partial differential equations filtering model (SOOPDE) to denoise speckle noise, then used the oriented gradient vector fields for to obtain skeletons. The full-field displacement of fractured nuclear graphite and the location of the crack tip were lastly measured under various loading conditions

Vein Pattern Extraction Using Near Infrared Imaging for Biometric Purposes

Biomedical verification has been broadly examined for many years and pulled in much consideration because of its huge potential security application. Vein is less prone to damage and almost improbable to copy than any other physiological as well as behavioural biometric features such as fingerprint, iris, face and voice recognition. This paper proposes an efficient vein extraction method on low quality vein images taken by a camera absorbing near infrared light (NIR camera). At first, the image is contrast enhanced using contrast limited adaptive histogram equalization (CLAHE); secondly, local threshold method is applied on small blocks of the image followed by several morphological operations such as fill, erosion, dilation, clean and bridge, performed sequentially, for better accuracy. Experimental results obtained for extraction show that the proposed method can reap better results with reduced complexity. After extraction, matching of the test image with the template images stored in the database are matched using minutiae (point-to-point pattern). An orientation detector which filters out missing or unnecessary or unnatural spurious minutiae pairings while simultaneously using path or ridge orientations to increase performance and similarity score calculation. Thus the obtained processed images can be used in biometric purposes which in turn enhances the security of the syste