Implementation of feature extraction based hand geometry in biometric identification system

Wirayuda, Tjokorda Agung Budi; Kuswanto, Didik Hari; Adhi, Habbi Ananto; Dayawati, Retno Novi

doi:10.1109/icoict.2013.6574583

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Hand images contain rich features, which could be utilized in terms of biometric identification, verification or classification. Examples of these are hand geometry [11] and finger geometry [12]. Despite the geometrical features being easy to acquire, they still result in low recognition performance and they are usually fused with other biometrics to enhance performance.…”

Section: Introductionmentioning

confidence: 99%

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

Al-Nima

Abdullah

Al-Kaltakchi

et al. 2017

Digital Signal Processing

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Section: Introductionmentioning

confidence: 99%

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

Al-Nima

Abdullah

Al-Kaltakchi

et al. 2017

Digital Signal Processing

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“…The feature selection for hand geometry-based biometrics has been a subject in many publications, such as [31][32][33]. Except for works using neural networks [34], the classical approach starts by determining various human hand dimensions.…”

Section: Feature Selection and Classification Algorithmmentioning

confidence: 99%

On the fly biometric identification system using hand‐geometry

2021

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Biometric‐enabled systems have become highly desirable to authenticate and identify people due to the proliferation of mobile technologies. Their application in general settings, such as automated border gates using ePassports or at entrances to buildings or rooms, has become popular as well. Regrettably, where current biometry scanning technologies are reliably suitable for single‐user devices, when hundreds or thousands of people need to be processed, shortcomings emerge. The requirement of physically coming to a halt during scanning poses a limitation to the throughput of the people processed. Additionally, since most of these systems require physical contact, hygienic concerns need to be considered, especially with contagious pandemics such as COVID‐19. In this article, we present a solution to the issue of limited throughput and hygienic concerns by developing a biometric system that allows identification on the fly. Using the experimental setup of our design, we demonstrate that hand‐related biometrics can be acquired on the fly. Furthermore, using a random decision forest classifier and our house‐collected database containing over 560 unique images, we inaugurated that the system is a viable solution for biometric identification on the fly.

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“…Most simple feature vectors are composed by grayscale profiles [160,80] or finger widths [40,35,58,35], finger widths and lengths [124,123,55,54] or finger widths and lengths together with fingertips information [41,189]. Other works also add hand and palm information about size or angles together with finger meassurements [164,72,183,188,82,192,48]. Surface and perimeter information [52,3] or even 3D information [175] have also been added to basic measurements to construct more complex feature vectors.…”

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confidence: 99%

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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Implementation of feature extraction based hand geometry in biometric identification system

Cited by 6 publications

References 2 publications

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

On the fly biometric identification system using hand‐geometry

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

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