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doi:10.1002/ima.v19:3

Cited by 29 publications

(3 citation statements)

References 223 publications

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“…Next, the contours of the fingers are localized using edge detection filter masks as described in [16]. The finger image is then normalized by fitting a straight line between the detected finger edges, whose parameters (a rotation and a translation) are used to create an affine transformation [9].…”

Section: A Baseline Systemsmentioning

confidence: 99%

Cross-database evaluation using an open finger vein sensor

Vanoni

Tomé

Shafey

et al. 2014

2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings

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Abstract-Finger vein recognition is a recent biometric application, which relies on the use of human finger vein patterns beneath the skin's surface. While several methods have been proposed in the literature, its applicability to uncontrolled scenarios has not yet been shown. To this purpose this paper first introduces the VERA database, a new challenging publicly available database of finger vein images. This corpus consists of 440 index finger images from 110 subjects collected with an open device in an uncontrolled way. Second, an evaluation of state-ofthe-art finger vein recognition systems is performed, both on the controlled UTFVP database and on the new VERA database. This is achieved using a new open source and extensible finger vein recognition framework, which allows fair and reproducible benchmarks. Experimental results show that challenging recording conditions such as misalignments of the fingers lead to an absolute degradation in equal error rate of 2.75% up to 24.10% on VERA when compared to the best performances on UTFVP.

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Section: A Baseline Systemsmentioning

confidence: 99%

Cross-database evaluation using an open finger vein sensor

Vanoni

Tomé

Shafey

et al. 2014

2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings

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“…25 The samples were acquired using a planar desktop scanner at a spatial resolution of 1200 dpi, and each image has a dimension of 128 px × 128 px. 25 The samples were acquired using a planar desktop scanner at a spatial resolution of 1200 dpi, and each image has a dimension of 128 px × 128 px.…”

Section: Plant Leavesmentioning

confidence: 99%

Experimental comparison of color spaces for material classification

Bello-Cerezo

Bianconi

Fernández

et al. 2016

J. Electron. Imaging

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This paper presents a comparison of color spaces for material classification. The study includes three device-independent (CIELAB, CIELUV, and CIE XYZ) and seven device-dependent spaces (RGB, HSV, YIQ, YUV, YC b C r , Ohta's I 1 I 2 I 3 , and RG-YeB-WhBl). The pros and cons of the different spaces and the procedures for converting color data among them are discussed in detail. An experiment based on 12 different image data sets was carried out to comparatively evaluate the performance of each space for material classification purposes. The results showed that CIELAB markedly outperformed the other spaces followed by HSV and CIELUV. Conversely, CIE XYZ came out as the worst performing space. Interestingly, no significant difference emerged among the performance of the other device-dependent spaces.

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“…Therefore, it is necessary to regularize the problem and narrow the search space by using some prior information about the attenuation map. [14][15][16][17] For the work presented here, it is assumed that the attenuation map can be obtained from the CT image through the following quadratic polynomial conversion: [2,9]…”

Section: Conversion Modelmentioning

confidence: 99%