Improved Cross Spectral Iris Matching Using Gradientface Based Normalization

Oktiana, Maulisa; Saddami, Khairun; Arnia, Fitri; Away, Yuwaldi; Munadi, Khairul

doi:10.1109/iceltics.2018.8548916

Cited by 3 publications

(4 citation statements)

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“…Previous works proved that the appropriate size and number of the filter are 7 × 7 with 8 bits [29]. The BSIF computation, as described in [25], is as follows:…”

Section: ) Binary Statistical Image Feature (Bsif)mentioning

confidence: 99%

“…Photometric normalization has been found to be useful to improve robustness in face recognition [22]. Photometric normalization was previously used as a pre-processing step in face recognition [23], [24], and when photometric normalization is combined with suitable descriptors, this combination achieved promising results [25]. The photometric normalization enhances the features with the same statistical properties.…”

Section: Introductionmentioning

confidence: 99%

“…The structures of the NIR and VIS imaging are similar and represent the same subject, although the NIR and VIS iris appearance are different. Our preliminary works in [25] combined the GRF with Binary Statistical Image Feature (BSIF) and Difference of Gaussian (DoG) filter. In this study, we concatenated the GRF with a Gabor filter, a DoG filter, BSIF, and local texture descriptors, namely an LBP, to obtain the best integration descriptors.…”

Section: Introductionmentioning

confidence: 99%

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Advances in Cross-Spectral Iris Recognition Using Integrated Gradientface-Based Normalization

et al. 2019

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Cross-spectral iris recognition represents the ability of the system to identify the iris images acquired in different electromagnetic spectrums. An iris captured in the near-infrared spectrum (NIR) is matched with an iris obtained in the visual light spectrum (VIS) to boost the recognition performance. In cross-spectral iris recognition, the illumination factor between NIR and VIS images significantly degrades the recognition performance. Therefore, the existing method only achieved recognition performance with an equal error rate (EER) larger than 5%, and it is a challenging issue for cross-spectral performance to have EER below 5%. In this paper, we improve iris recognition performance by concatenating the Gradientfacesbased normalization technique (GRF) to a standard (conventional) iris recognition method to alleviate the illumination effect. In addition, we integrate the GRF with a Gabor filter, a difference of Gaussian (DoG) filter, and texture descriptors, namely a binary statistical image feature (BSIF) and a local binary pattern (LBP). The experimental results show that the GRF can boost the cross-spectral iris recognition performance with an EER equals to 1.69%. In addition, the best cross-spectral iris recognition performance is achieved when the GRF is integrated with the Gabor filter and the BSIF.

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“…Previous works proved that the appropriate size and number of the filter are 7 × 7 with 8 bits [29]. The BSIF computation, as described in [25], is as follows:…”

Section: ) Binary Statistical Image Feature (Bsif)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advances in Cross-Spectral Iris Recognition Using Integrated Gradientface-Based Normalization

et al. 2019

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“…Existing studies on cross-spectral iris recognition primarily have a feature-based approach [13,14,15,16,17], which is significantly affected by parameters in the feature extraction process, such as spatial position and orientation, as well as iris image acquisition conditions that can degrade recognition performance. To address these limitations, we propose cross-spectral iris recognition using the phase of NIR and VIS iris images.…”

Section: Introductionmentioning

confidence: 99%

Cross-spectral iris recognition using phase-based matching and homomorphic filtering

Oktiana

Horiuchi

Hirai

et al. 2020

Heliyon

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In cross-spectral iris recognition, different spectral bands are used to obtain rich information of the human iris. Previous studies on cross-spectral iris recognition are based primarily on feature-based approaches, which are prone to the changes in parameters in the feature extraction process, such as spatial position and iris image acquisition conditions. These parameters can degrade iris recognition performance. In this paper, we present a phase-based approach for cross-spectral iris recognition using phase-only correlation (POC) and band-limited phase-only correlation (BLPOC). A phase-based iris recognition system recognizes an iris using the phase information contained in the iris image; therefore, its performance is not affected by feature extraction parameters. However, the performance of a phase-based cross-spectral iris recognition is strongly influenced by specular reflection. Different illumination conditions may produce different iris images from the same subject. To overcome this challenge, we integrate a photometric normalization technique -homomorphic filteringwith phasebased cross-spectral iris recognition. The experimental results reveal that the proposed technique achieved an excellent matching performance with an equal error rate of 0.59% and a genuine acceptance rate of 95%.

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A Survey on Iris Recognition Systems in Cross-Spectral Domain

Al-Kardhi,

Al-Juboori

2023

2023 International Conference on Information Technology, Applied Mathematics and Statistics (ICITAMS)

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Improved Cross Spectral Iris Matching Using Gradientface Based Normalization

Cited by 3 publications

References 10 publications

Advances in Cross-Spectral Iris Recognition Using Integrated Gradientface-Based Normalization

Advances in Cross-Spectral Iris Recognition Using Integrated Gradientface-Based Normalization

Cross-spectral iris recognition using phase-based matching and homomorphic filtering

A Survey on Iris Recognition Systems in Cross-Spectral Domain

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