Biometric-oriented Iris Identification Based on Mathematical Morphology

Mira, Joaquim De; Neto, Hugo Vieira; Neves, Eduardo Borba; Schneider, Fábio Kurt

doi:10.1007/s11265-013-0861-0

Cited by 17 publications

(5 citation statements)

References 17 publications

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“…In order to test the proposed algorithm, we first apply it with the CASIA-Iris-Interval database, which contains the iris images of 249 subjects with a total of 2639 images [14], and then with the UBIRIS database, containing 1877 noisy images collected from 241 eyes [15]. We have compared the results with the method proposed by Masek [11], Daugmana [32], Radu [40], Mira [41] and Radman [42] using the EER, AUC and ARR statistics. The results are shown in Tables 3 and 4.…”

Section: -Experimental Resultsmentioning

confidence: 99%

“…Masek [11] Daugmana [32] Mira [41] Proposed method EER % Besides, since the iris is an epigenetic phenotypic feature (which means that even twins have their unique iris information), the matching process should also work with image of iris captured from twins. This case has also been tested with the CASIA-Iris-Twins database (Figure 14), which contains iris images of 100 pairs of twins [14].…”

Section: Casia-iris-intervalmentioning

confidence: 99%

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Algorithm for Iris recognition based on contourlet Transform and Entropy

Ezzaki

Idrissi

Moreno

et al. 2020

ELCVIA

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The iris is one of the most secure biometric types of information that is widely employed in authentication systems. In this paper we present a method for iris recognition based on the Contourlet Transform and Shannon Entropy which entails (i) the detection and segmentation of the iris, (ii) its normalization, (iii) the application of the Contourlet Transform, (iv) the generation of the iris descriptor, and (v) the matching between the query iris and those in the database. The proposed method has been tested with images taken from the popular CASIA-V4 and UBIRIS.v1 datasets and compared against other six recent iris recognition algorithms using the statistics EER, AUC and ARR. The results show a higher performance of the proposed method with a reduced computation time.

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Section: -Experimental Resultsmentioning

confidence: 99%

Section: Casia-iris-intervalmentioning

confidence: 99%

Algorithm for Iris recognition based on contourlet Transform and Entropy

Ezzaki

Idrissi

Moreno

et al. 2020

ELCVIA

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“…The results obtained in this table demonstrate the recognition efficiency of the proposed biometric system. Table 3 shows the comparison between our proposed method and others methods [29]- [31] tested with samples iris images from CASIA-V4-Interval database.…”

Section: Brief Resultsmentioning

confidence: 99%

Uncertainty Theories Based Iris Recognition System

Bellaaj

2018

ELCVIA

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The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics.

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“…Contrasted and customary verification strategies dependent on passwords and recognizable proof cards, biometric verification is viewed as progressively solid and helpful [1]. Moreover, fingerprint verification has been generally applied in numerous fields by utilizing biometric characteristics, for example, unique finger impression [2], iris [3], and facial examples [4], which can be gathered from different sensors [5]- [9]. In a biometric verification framework, the database proprietor, for example, the FBI who is dependable to deal with the national fingerprints database may want to redistribute the huge biometric information to the cloud server (e.g., Amazon) to dispose of the costly capacity and calculation costs.…”

Section: Introductionmentioning

confidence: 99%

Secure and Fast Biometric Verification Scheme for Cloud Storage Systems

Reddy*,

Anuradha

2019

IJITEE

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Biometric verification of has turned out to be progressively well known as of late. With the improvement of cloud computing, database owners are enthused to outsource the enormous size of biometric information and verification activity to the cloud to dispose of the costly capacity and calculation costs, which anyway carries potential dangers to clients' security. This article presents an proficient and protection saving biometric verification outsourcing plan. In particular, the biometric information is encoded and redistributed to the cloud server. To execute a biometric verification, the database owner encodes the query information and submits it to the cloud. The cloud performs verification tasks over the encoded database and returns the outcome to the database owner. A careful security examination shows the proposed plan is secure regardless of whether aggressors can produce verification demands and intrigue with the cloud. Contrasted and past conventions, trial results demonstrate the proposed plan accomplishes a better presentation in both preparation and verification systems.

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Biometric-oriented Iris Identification Based on Mathematical Morphology

Cited by 17 publications

References 17 publications

Algorithm for Iris recognition based on contourlet Transform and Entropy

Algorithm for Iris recognition based on contourlet Transform and Entropy

Uncertainty Theories Based Iris Recognition System

Secure and Fast Biometric Verification Scheme for Cloud Storage Systems

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