A cross-sensor evaluation of three commercial iris cameras for iris biometrics

Connaughton, Ryan; Sgroi, Amanda; Bowyer, Kevin W.; Flynn, Patrick J.

doi:10.1109/cvprw.2011.5981814

Cited by 22 publications

(10 citation statements)

References 7 publications

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“…For each probe image, classification result is obtained from the classifier and selective enhancements are then applied to iris images according to Equations (4) to (6). For the MSI database, in Equation (4), ⊖ and ⊕ are the difference and sum operators respectively and the parameters 1 = 0.65 and 2 = 0.25.…”

Section: Iris Recognition With Camera Interoperabilitymentioning

confidence: 99%

“…Connaughton et al [6] also studied cross-camera iris recognition using two iris cameras and three different matching algorithms. They concluded that there is no clear pattern as to whether single-camera or cross-camera is better and further stated that it was the combination of the algorithm and the camera which determines the overall accuracy of an iris recognition system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On iris camera interoperability

Arora

Vatsa

Singh

et al. 2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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Section: Iris Recognition With Camera Interoperabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On iris camera interoperability

Arora

Vatsa

Singh

et al. 2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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“…In controlled environments, iris recognition is indeed extremely accurate but recent studies suggest that the iris texture is affected by covariates like pupil dilation [30] and sensor interoperability [3,11]. Presence of both textured and transparent soft contact lenses is a another issue that may cause severe degradation in iris recognition performance in terms of false non-match rate (FNMR) [4,5,35,56].…”

Section: Introductionmentioning

confidence: 99%

Contact lens detection in iris images

Komulainen¹,

Hadid²

2017

Iris and Periocular Biometric Recognition

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Iris texture provides the means for extremely accurate uni-modal person identification. However, the accuracy of iris based biometric systems is sensitive to the presence of contact lenses in acquired sample images. This is especially true in the case of textured (cosmetic) contact lenses that can be effectively used to obscure the original iris texture of a subject and consequently to perform presentation attacks. Since also transparent contact lenses can degrade matching rates, automatic detection and classification of different contact lens types is needed in order to improve the robustness of iris based biometric systems. This chapter introduces the problem of contact lens detection with particular focus on cosmetic contact lenses. The state of the art is analysed thoroughly and a case study on generalised textured contact lens detection is provided. The potential future research directions are also discussed.

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“…Based on the appliance, a biometric system may operate either in verification mode or identification mode [4] (Jain et al 2004),In the verification mode, a person's identity is validated through comparison between the captured biometric data and its own biometric templates stored in the database of the system. In such a system, an individual who is desirable of recognition gives a claim of identity, usually a PIN and a one-to-one comparison is done by the system to determine if claim is true or not [8] (Wayman 2001).…”

Section: Operations Of a Biometric Systemmentioning

confidence: 99%

“…In the first place confirmations of biometrics showed up in 29.000bc when the cave dwellers utilized their fingerprints to sign their drawings. The initially recorded proof of utilizing biometric confirmation was as a part of old Egypt [11].…”

Section: Introductionmentioning

confidence: 99%

Design of Biometric Authentication System using Three Basic Human Traits

2016

IJSR

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Todays the single biometrics feature is not sufficient to provide secure authentication. Most of the multi-modal techniques are lacking in security aspect. Biometric system utilize in various system for the identification or authentication approval. Biometric authentication system utilizes various biometric traits for the matching between various biometric traits. These feature values that have been computed by using various approaches for biometric traits like face, finger and iris. These feature value has been stored in database for matching purpose using distance classifier. After fusion these values has been computed for all the images present in the database and then test images has been selected and undergoes matching process on the basis of distance classifier. Min distance computed represents the maximum matching of the feature of different biometric traits. Parameter selected for the performance evolution of proposed system are FAR (False Acceptance Rate), FRR (False Rejection Rate) and ACCURACY.

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A cross-sensor evaluation of three commercial iris cameras for iris biometrics

Cited by 22 publications

References 7 publications

On iris camera interoperability

On iris camera interoperability

Contact lens detection in iris images

Design of Biometric Authentication System using Three Basic Human Traits

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