The Biosecure Geometry-Based System for Hand Modality

Fouquier, Geoffroy; Likforman, Laurence; Darbon, Jérôme; Sankur, B.

doi:10.1109/icassp.2007.366029

Cited by 11 publications

(11 citation statements)

References 8 publications

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“…Fouquier et al proposed a method based on the projection of finger boundaries on the major axis of the fingers. 66 They segmented the fingers using fingertips and interfinger valleys, and computed the histogram of the distances from the finger boundary to the major axis of each finger. The histograms, smoothed with a Gaussian kernel, constitute their feature vectors.…”

Section: The Fingersmentioning

confidence: 99%

Comparative analysis of global hand appearance-based person recognition

Dutağacı

Sankur

Yörük

2008

J. Electron. Imaging

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Section: The Fingersmentioning

confidence: 99%

Comparative analysis of global hand appearance-based person recognition

Dutağacı

Sankur

Yörük

2008

J. Electron. Imaging

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“…For the hand modality, the BioSecure reference system [17] uses geometry of fingers for recognition purposes. The selected database is the Biosecure database [17]. 5.…”

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confidence: 99%

Open Source Reference Systems for Biometric Verification of Identity

Mayoue¹,

Petrovska‐Delacrétaz²

IFIP – The International Federation for Information Processing

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Abstract. This paper focuses on the common evaluation framework which was developed by the BioSecure Network of Excellence during the European FP6 project BioSecure (Biometrics for Secure authentication). This framework, which is composed of open-source reference systems, publicly available databases, assessment protocols and benchmarking results, introduces a new experimental methodology for conducting, reporting and comparing experiments for biometric systems, participating to standardisation efforts. Its use will permit to make a link between different published works. It provides also the necessary tools to assure the reproducibility of the benchmarking biometric experiments. This framework can be considered as a re-liable and innovative process to evaluate the progress of research in the field of bio-metrics.

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“…Later methods extract geometrical information about the hand included in the palm and fingers and are more widespread mainly due to its simplicity. 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].…”

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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.…”

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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,…”

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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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The Biosecure Geometry-Based System for Hand Modality

Cited by 11 publications

References 8 publications

Comparative analysis of global hand appearance-based person recognition

Comparative analysis of global hand appearance-based person recognition

Open Source Reference Systems for Biometric Verification of Identity

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

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