Biometric authentication from low resolution hand images using radon transform

Mostayed, Ahmed; Kabir, Md. Ekramul; Khan, Saurav Zaman; Mazumder, Md. Mynuddin Gani

doi:10.1109/iccit.2009.5407305

Cited by 18 publications

(13 citation statements)

References 7 publications

(16 reference statements)

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“…Finally, hand recognition using low-cost devices is an important issue. For instance, Santos-Sierra et al 25 present an algorithm to segment hand images using multilayer graphs and Mostayed et al 26 use low resolution hand images and compute a set of position invariant features using the Radon transform.…”

Section: Related Workmentioning

confidence: 99%

A Contactless Identification System Based on Hand Shape Features

Bernardos

Sánchez

Portillo

et al. 2015

Procedia Computer Science

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This paper aims at studying the viability of setting up a contactless identification system based on hand features, with the objective of integrating this functionality as part of different services for smart spaces. The final identification solution will rely on a commercial 3D sensor (i.e. Leap Motion) for palm feature capture. To evaluate the significance of different hand features and the performance of different classification algorithms, 21 users have contributed to build a testing dataset. For each user, the morphology of each of his/her hands is gathered from 52 features, which include bones length and width, palm characteristics and relative distance relationships among fingers, palm center and wrist. In order to get consistent samples and guarantee the best performance for the device, the data collection system includes sweet spot control; this functionality guides the users to place the hand in the best position and orientation with respect to the device. The selected classification strategies -nearest neighbor, supported vector machine, multilayer perceptron, logistic regression and tree algorithms -have been evaluated through available Weka implementations. We have found that relative distances sketching the hand pose are more significant than pure morphological features. On this feature set, the highest correct classified instances (CCI) rate (>96%) is reached through the multilayer perceptron algorithm, although all the evaluated classifiers provide a CCI rate above 90%. Results also show how these algorithms perform when the number of users in the database change and their sensitivity to the number of training samples. Among the considered algorithms, there are different alternatives that are accurate enough for non-critical, immediate response applications.

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Section: Related Workmentioning

confidence: 99%

A Contactless Identification System Based on Hand Shape Features

Bernardos

Sánchez

Portillo

et al. 2015

Procedia Computer Science

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“…The image of the veins that was extracted in the previous step allow us to create a database of prototypes with pattern that are in the database (template) by authenticating the identity of an individual, will either accept the person, or reject it. Instead of the identification [41,42], the system will identify the right person. In order to evaluate their system testing performance, [31] uses a dataset of 500 persons of different ages above 16 and of different gender, each has 10 images per person was acquired at different intervals, 5 images for left hand and 5 images for right hand.…”

Section: Figmentioning

confidence: 99%

Dorsal Hand Vein Identification Based on Binary Particle Swarm Optimization

2017

J Inf Process Syst

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The dorsal hand vein biometric system developed has a main objective and specific targets; to get an electronic signature using a secure signature device. In this paper, we present our signature device with its different aims; respectively: The extraction of the dorsal veins from the images that were acquired through an infrared device. For each identification, we need the representation of the veins in the form of shape descriptors, which are invariant to translation, rotation and scaling; this extracted descriptor vector is the input of the matching step. The optimization decision system settings match the choice of threshold that allows accepting/rejecting a person, and selection of the most relevant descriptors, to minimize both FAR and FRR errors. The final decision for identification based descriptors selected by the PSO hybrid binary give a FAR =0% and FRR=0% as results.

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“…Former methods analyse the information about the hand shape extracted from the hand contour and include approaches such as contour alignment [78,195,194], contour angles [11], b-spline curves [118], zernike moments [5], eigenhands [177], independent component analysis [200], radon transform [128] or contour curvature and distance to the centroid [36]. Later methods extract geometrical information about the hand included in the palm and fingers and are more widespread mainly due to its simplicity.…”

Section: Introductionmentioning

confidence: 99%

“…These methods include global thresholding, which is the easiest but most extended approach as commented above, and more sophisticated and well known methods such as Graph Cuts. Thresholding segmentation is a really simple solution which requires low computational resources but entail very specific capturing conditions to provide a good per- [153,189,137,156,65,70,141,27,51,111,195,72,11,194,155,154,115,7,56,5,53,100,58,197,8,55,196,128,3,179,147,127,106,87 Table 2. : Hand segmentation for biometric applications using visual spectrum images classified according to the nature of the testing images in terms of pose restrictions and environmental conditions.…”

mentioning

confidence: 99%

“…Due to different biometric traits can be extracted from hand images such as hand geometry [47,35], palmprint [97] or hand veins [208,191], hand can be considered multimodal itself and, thus, it is possible to improve the system performance by exploiting the information provided by its different traits. In this manner, some hand multimodal systems have been developed during the last years combining hand 202,98,95,43,22,174,96,190,101,81,68,121,62,149,69,209,206,193,108,85,148,152,139,66 [189,65,51,72,11,195,194,5,200,58,55,54,128,3,39,87,6] Palmprint [70,…”

mentioning

confidence: 99%

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Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

Sánchez¹

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In this thesis different techniques of image processing, machine learning and information fusion have been analysed in relation to their applicability to contact-less hand biometrics. To this end, a modular and configurable system that explodes the multimodal nature of the hand to increase its robustness and accuracy have been designed, implemented and evaluated. Given the fact that different applications have different accuracy and time performance needs, the evaluation is aimed to provide a fair comparative of methods under different environmental conditions that helps to adapt the system to the specific requirements of a concrete final application.A correct hand segmentation is necessary to extract reliable and invariant biometric features. For this reason, a comparative of different segmentation methods that include well-known methods such as global thresholding and graph cuts as well as a novelty flooding-based method which combines different image-based segmentation approaches. These methods have been compared using diverse datasets of images which cover a wide spectrum of capturing conditions.On the other hand, a comprehensive evaluation of different palmprint feature extraction methods comprising Gabor and Sobel filters, Local Binary Patterns, Local Derivative Patterns and Curvelets has been carried out. Different parameter configurations have also been tested with the aim of finding out which arrangement provides better results for each method. In addition to palmprint, also hand geometry features have been extracted. This evaluation includes also two different feature matching approaches: distance-based and Support Vector Machines.In addition, it has also been evaluated the feasibility of combining different feature extraction methods to yield into a more precise and robust multimodal solution. Two different levels for fusing the biometric information have been compared: score-level and feature-level.Finally, an evaluation methodology that allows for a fair comparison between different methods has been proposed. In particular, an evaluation protocol is offered with the aim of not only obtaining an extensive evaluation of the complete system under different environmental conditions, and testing multiple combinations of methods for each module, but also providing a basis against which to compare future research.Keywords: Accuracy, Biometrics, Computation Requirements, Configurable, Curvelets, Distance, Evaluation, Feature-level fusion, Flooding, Fusion, Gabor, Graph-Cuts, Hand Geometry, Local Binary Patterns, Local Derivative Patterns, Modular, Multimodal, Palmprint, Score-level fusion, Segmentation, Sobel, Support Vector Machines, Thresholding, Varied Environmental Conditions. R E S U M E NEn esta tesis se han analizado diferentes técnicas de procesado de imagen, aprendizaje automático y fusión de la información en relación a su aplicabilidad a la biometría de mano sin contacto. Para ello se ha diseñado, implementado y evaluado un sistema modular y configurable que explota la naturaleza multimodal de la...

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Biometric authentication from low resolution hand images using radon transform

Cited by 18 publications

References 7 publications

A Contactless Identification System Based on Hand Shape Features

A Contactless Identification System Based on Hand Shape Features

Dorsal Hand Vein Identification Based on Binary Particle Swarm Optimization

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

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