Adaboost and multi-orientation 2D Gabor-based noisy iris recognition

Wang, Qi; Zhang, Xiangde; Li, Mingqi; Dong, Xiaopeng; Zhou, Qunhua; Yin, Yu

doi:10.1016/j.patrec.2011.08.014

Cited by 63 publications

(29 citation statements)

References 16 publications

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“…In [9] scalar based Eigen space is used. In [10] Gabor filter is used for iris detection. In [11] the author proposes an iris recognition technique based on Zigzag Collarette Region and Asymmetrical Support Vector Machines.…”

Section: Past Approachesmentioning

confidence: 99%

Improved Iris Recognition in 2D Eigen Space

Das¹,

Parekh²

2012

IJCA

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This paper a new biometric method for personal identification is been presented by iris identification of a person in lower dimensionality and reduced template size than the other previous approaches in 2D Eigen space, so that it can be use for verification in application areas . Here the iris images are expressed in lower dimension, re-tending its features by using covariance matrix and Eigen matrix to a covariant-Eigen space vector. The proposed approach is also suitable to work on half iris image. The proposed approach shows high accurate result.

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Section: Past Approachesmentioning

confidence: 99%

Improved Iris Recognition in 2D Eigen Space

Das¹,

Parekh²

2012

IJCA

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“…Extracting the significant features from these images which are having high imaging variations is a challenging task. Recent works [15,10,26,18,23,20,22,21] have developed algorithms for non-cooperative and noisy iris recognition. All of these works have taken either right eye or left eye for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

“…Prominant among them are Gabor filter [7,26], logGabor filter [20], Laplacian of Gaussian [27], wavelet filter banks [4] and Fourier and Discrete Cosine Transformation (DCT) [14]. Daugmans phase based bit encoding technique and binary representation of iris patterns is adopted by many authors.…”

Section: Introductionmentioning

confidence: 99%

“…The author opines that there is still scope for possible improvement in noisy, at-a-distance iris recognition systems and suggested several themes on which future works can be carried out. One of them is matching the information from different parts of the iris and fusing the result as discussed in [26] and another one is fusion of the features from multiple sources of segmented iris and eye image [18,23,20]. Motivated by these works and exploiting the ground truth of iris biometrics, that states: iris patterns of left and right eyes of same person are different, we propose this work which explores features from both left and right irises and combines them at bit level.…”

Section: Introductionmentioning

confidence: 99%

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Steerable Riesz wavelet based approach for iris recognition

Shekar

Bhat²

2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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In this paper, we explore the applicability of first and second order monogenic Steerable Riesz wavelet components for iris recognition. These wavelets provide powerful mechanism to extract the invariant as well as covariant local variations of iris patterns. Unlike other existing methods where sole iris (either left or right) is used for recognition, in our work, we extract the features from both left and right irises, encode them separately and perform bit level fusion. Extensive experimentation has been conducted on the benchmark databases namely, IITD, MMU v-2 and CA-SIA v-4 distance to exhibit the performance of the proposed method. Comparative analysis is also performed with the state of the art methods to justify the suitability of the proposed method for iris recognition.

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“…Wildes et al [9][10][11] extracted features by Gauss-Laplace pyramid decomposition and matched them with correlation filters. Sun et al [14][15][16][17] described the statistical features of the iris region structure with a local binary pattern (LBP) histogram and made a structural match of iris image blocks with graph matching. Boles 12 and Ma et al 13 proposed a zero-crossing representation of different resolutions for feature extraction with a one-dimensional wavelet and then matched the two different functions.…”

Section: Introductionmentioning

confidence: 99%

Secondary iris recognition method based on local energy-orientation feature

Huo

Liu

Zhu

et al. 2015

J. Electron. Imaging

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This paper proposes a secondary iris recognition based on local features. The application of the energy-orientation feature (EOF) by two-dimensional Gabor filter to the extraction of the iris goes before the first recognition by the threshold of similarity, which sets the whole iris database into two categories-a correctly recognized class and a class to be recognized. Therefore, the former are accepted and the latter are transformed by histogram to achieve an energy-orientation histogram feature (EOHF), which is followed by a second recognition with the chi-square distance. The experiment has proved that the proposed method, because of its higher correct recognition rate, could be designated as the most efficient and effective among its companion studies in iris recognition algorithms.

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Adaboost and multi-orientation 2D Gabor-based noisy iris recognition

Cited by 63 publications

References 16 publications

Improved Iris Recognition in 2D Eigen Space

Improved Iris Recognition in 2D Eigen Space

Steerable Riesz wavelet based approach for iris recognition

Secondary iris recognition method based on local energy-orientation feature

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