The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance

Proença, Hugo; Filipe, Sílvio; Santos, Ricardo; Oliveira, João Filipe; Alexandre, Luı́s A.

doi:10.1109/tpami.2009.66

Cited by 452 publications

(243 citation statements)

References 13 publications

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“…In 2010, the University of Beira introduced another version of UBIRIS database called UBIRIS.v2 [13] iris database, which is a multisession iris database that contains images captured at a distance of 4 to 8 meters, on the move and lit with a visible wavelength light source. The reason behind the creation of UBIRIS.v2 database is to constitute a new iris dataset collected under a natural or artificial visible wavelength light source, with characteristics far away from ideal imaging framework.…”

Section: Ubiris V2 Databasementioning

confidence: 99%

A Short Survey of IRIS Images Databases

Alrifaee¹,

Abdallah²,

Okush³

2017

IJMA

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Section: Ubiris V2 Databasementioning

confidence: 99%

A Short Survey of IRIS Images Databases

Alrifaee¹,

Abdallah²,

Okush³

2017

IJMA

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“…They used the visible spectrum eye images of UBIIRIS v2 [28] dataset which contained slight appearance variations. Xu et al [16] proposed Walsh Transform based local binary patterns (WLBP).…”

Section: Survey Of Periocular Biometrics Researchmentioning

confidence: 99%

Periocular region-based person identification in the visible, infrared and hyperspectral imagery

et al. 2015

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Face recognition performance degrades significantly under occlusions that occur intentionally or unintentionally due to head gear or hair style. In many incidents captured by surveillance videos, the offenders cover their faces leaving only the periocular region visible. We present an extensive study on periocular region based person identification in video. While, previous techniques have handpicked a single best frame from videos, we formulate, for the first time, periocular region based person identification in video as an image-set classification problem. For thorough analysis, we perform experiments on periocular regions extracted automatically from RGB videos, NIR videos and hyperspectral image cubes. Each image-set is represented by four heterogeneous feature types and classified with six state-of-the-art image-set classification algorithms. We propose a novel two stage inverse Error Weighted Fusion algorithm for feature and classifier score fusion. The proposed two stage fusion is superior to single stage fusion. Comprehensive experiments were performed on four standard datasets, MBGC NIR and visible spectrum [1], CMU Hyperspectral [2] and UBIPr [3]. We obtained average rank-1 recognition rates of 99.8, 98.5, 97.2, and 99.5% respectively which are significantly higher than the existing state of the art. Our results demonstrate the feasibility of image-set based periocular biometrics for real world applications.

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“…As experimental dataset, we use data from the following databases: BioSec [20], CASIA-Iris Inverval v3 [21], IIT Delhi v1.0 [22], MobBIO [23] and UBIRIS v2 [24]. A summary of the used subset of these databases is given in Table 1.…”

Section: Databases and Experimental Protocolmentioning

confidence: 99%

Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images

Alonso‐Fernandez

Mikaelyan

Bigün

2015

3rd International Workshop on Biometrics and Forensics (IWBF 2015)

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Periocular refers to the facial region in the eye vicinity. It can be easily obtained with existing face and iris setups, and it appears in iris images, so its fusion with the iris texture has a potential to improve the overall recognition. It is also suggested that iris is more suited to near-infrared (NIR) illumination, whereas the periocular modality is best for visible (VW) illumination. Here, we evaluate three periocular and three iris matchers based on different features. As experimental data, we use five databases, three acquired with a close-up NIR camera, and two in VW light with a webcam and a digital camera. We observe that the iris matchers perform better than the periocular matchers with NIR data, and the opposite with VW data. However, in both cases, their fusion can provide additional performance improvements. This is specially relevant with VW data, where the iris matchers perform significantly worse (due to low resolution), but they are still able to complement the periocular modality.

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The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance

Cited by 452 publications

References 13 publications

A Short Survey of IRIS Images Databases

A Short Survey of IRIS Images Databases

Periocular region-based person identification in the visible, infrared and hyperspectral imagery

Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images

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