Efficient BSIF-based near-infrared iris recognition

Rathgeb, Christian; Struck, F.; Busch, Christoph

doi:10.1109/ipta.2016.7820932

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…In contrast to texture description, some researchers have lately tried to describe alternative iris features, including salient interest points [1,2] and human-interpretable features [5]. Nonetheless, a prominent set of works still has been focusing on iris texture, specifically employing general-purpose texture descriptors such as Local Binary Patterns (LBP) [20], Local Phase Quantization (LPQ) [21], and BSIF [27,28], the latter descriptor reportedly constituting better iris recognition systems.…”

Section: Related Workmentioning

confidence: 99%

“…It means that even if the mutual rotation between the template and the probe is non-zero, the only thing that changes is the spatial location of elements within the normalized image, hence the resulting histograms are the same. Rathgeb et al [28] proposed to calculate histograms locally in the predefined iris image patches (however, without excluding the occluded iris areas) and to binarize the histograms to calculate a compact iris image representation. We also added this method for comparison in Section 4.3.…”

Section: Domain Definition: Iris Recognitionmentioning

confidence: 99%

“…Figures 10 present comparison score distributions, EER and d' obtained on the test set for all BSIF-based methods. To relate these approaches to other methods, we add results obtained for the OSIRIS matcher [22] and Rathgeb et al [28] approach distributed along with the USIT package [29], as shown in Fig. 11.…”

Section: Testing and Comparison Of Solutionsmentioning

confidence: 99%

“…Binarized Statistical Image Features (BSIF) have been shown to be effective in iris recognition [27,28] as well as in iris presentation attack detection [9,10,26]. All these approaches applied the standard BSIF filters provided with the original paper [11].…”

Section: Introductionmentioning

confidence: 99%

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Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition

Czajka

Moreira

Bowyer

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Binarized statistical image features (BSIF) have been successfully used for texture analysis in many computer vision tasks, including iris recognition and biometric presentation attack detection. One important point is that all applications of BSIF in iris recognition have used the original BSIF filters, which were trained on image patches extracted from natural images. This paper tests the question of whether domain-specific BSIF can give better performance than the default BSIF. The second important point is in the selection of image patches to use in training for BSIF. Can image patches derived from eye-tracking experiments, in which humans perform an iris recognition task, give better performance than random patches? Our results say that (1) domain-specific BSIF features can outperform the default BSIF features, and (2) selecting image patches in a task-specific manner guided by human performance can out-perform selecting random patches. These results are important because BSIF is often regarded as a generic texture tool that does not need any domain adaptation, and human-task-guided selection of patches for training has never (to our knowledge) been done. This paper follows the reproducible research requirements, and the new iris-domain-specific BSIF filters, the patches used in filter training, the database used in testing and the source codes of the designed iris recognition method are made available along with this paper to facilitate applications of this concept. arXiv:1807.05248v2 [cs.CV]

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Section: Related Workmentioning

confidence: 99%

Section: Domain Definition: Iris Recognitionmentioning

confidence: 99%

Section: Testing and Comparison Of Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition

Czajka

Moreira

Bowyer

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…BSIF have been used for several applications including biometrics from iris images [17,12,24]. In this work, a gender classification algorithm using normalised NIR iris images is proposed.…”

Section: Related Work 21 Gender Classificationmentioning

confidence: 99%

Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features

Tapia

Arellano

2019

2019 International Conference on Biometrics (ICB)

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Soft biometric information such as gender can contribute to many applications like as identification and security. This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors. It uses the same pipeline for iris recognition systems consisting of iris segmentation, normalisation and then classification. Experiments show that applying BSIF is not straightforward since it can create artificial textures causing misclassification. In order to overcome this limitation, a new set of filters was trained from eye images and different sized filters with padding bands were tested on a subject-disjoint database. A Modified-BSIF (MBSIF) method was implemented. The latter achieved better gender classification results (94.6% and 91.33% for the left and right eye respectively). These results are competitive with the state of the art in gender classification. In an additional contribution, a novel gender labelled database was created and it will be available upon request.

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A generic feature-independent pyramid multilevel model for Arabic handwriting recognition

Korichi

Slatnia

Aiadi

et al. 2022

Multimed Tools Appl

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Efficient BSIF-based near-infrared iris recognition

Cited by 8 publications

References 22 publications

Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition

Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition

Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features

A generic feature-independent pyramid multilevel model for Arabic handwriting recognition

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