Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation

Ávila, Carmen Sánchez; Sánchez-Reillo, Raúl

doi:10.1016/j.patcog.2004.07.004

Cited by 84 publications

(28 citation statements)

References 21 publications

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“…2D Gabor filters [24] measure characteristics in both space and frequency domains, so are well suited to describing local structural information which corresponds to spatial frequencies (scale), location, and direction. 2D Gabor filters usually have even-symmetry and odd-symmetry, and can be expressed as…”

Section: Local Textureness Measurementioning

confidence: 99%

Automatic texture exemplar extraction based on global and local textureness measures

Lyu

Wen

2018

Comp. Visual Media

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Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques emphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, obtaining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant postprocessing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly crop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured patches from non-textured patches, in conjunction with SVM prediction. To identify real texture exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure (using binary Gabor pattern (BGP)) and dominant color features (using color histograms) between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures. We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures manually selected by an artist and a statistical study demonstrate its effectiveness.

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Section: Local Textureness Measurementioning

confidence: 99%

Automatic texture exemplar extraction based on global and local textureness measures

Lyu

Wen

2018

Comp. Visual Media

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“…In image processing, a Gabor filter, named after Dennis Gabor, is a linear filter used for edge detection [10]. Frequency and orientation representations of Gabor filters are similar to those of the human visual system, and they have been found to be particularly appropriate for texture representation and discrimination.…”

Section: D) Gabor Filtermentioning

confidence: 99%

Evaluation of Iris Recognition System on Multiple Feature Extraction Algorithms and its Combinations

Ashwini¹,

Imran²,

Alsaade³

2015

IJCATR

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Multi-algorithmic approach to enhancing the accuracy of iris recognition system is proposed and investigated. In this system, features are extracted from the iris using various feature extraction algorithms, namely LPQ, LBP, Gabor Filter, Haar, Db8 and Db16. Based on the experimental results, it is demonstrated that Mutli-algorithms Iris Recognition System is performing better than the unimodal system. The accuracy improvement offered by the proposed approach also showed that using more than two feature extraction algorithms in extracting the iris system might decrease the system performance. This is due to redundant features. The paper presents a detailed description of the experiments and provides an analysis of the performance of the proposed method.

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“…The variance of orientation has been used as feature vector [29]. The query and template images have been matched using the L 2 norm [30].…”

Section: Computation Of Features Variancementioning

confidence: 99%

Robust Iris Recognition based on Orientation Field

Patil¹

2014

IJCA

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Recently, with an increasing demand on security biometric personal identification has been receiving extensive attention. Among many biometrics techniques, iris recognition is one of the most promising approached due to its high reliability. This paper present a robust iris recognition technique based on orientation field. Initially the eye images have been localized in circular form by using Hough transform. Than flat bed form of iris is generated using Daugman's homogeneous rubber sheet model. From the view point of texture analysis, the local spatial patterns in an iris mainly involve orientation and frequency information. Local image structure plays an important role in iris recognition, so an intuitive idea of iris pattern representation is based on the geometric structure of image data in small region. In our work localized iris image is divided into number of non-overlapping blocks. The blocks orientation has been determined from the pixel gradient orientation based on averaging and optimization. The gradient magnitudes have been computed using Sobel gradient operators. The variance of orientation has been used as feature vector. L 2 norm is used to compute a similarity score for the query and template images. The performance of the implemented algorithms has been evaluated using Receiver Operating Characteristics (ROC) and experimental results are reported.

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Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation

Cited by 84 publications

References 21 publications

Automatic texture exemplar extraction based on global and local textureness measures

Automatic texture exemplar extraction based on global and local textureness measures

Evaluation of Iris Recognition System on Multiple Feature Extraction Algorithms and its Combinations

Robust Iris Recognition based on Orientation Field

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