FIRME: Face and Iris Recognition for Mobile Engagement

Marsico, Maria De; Galdi, Chiara; Nappi, Michele; Riccio, Daniel

doi:10.1016/j.imavis.2013.12.014

Cited by 131 publications

(57 citation statements)

References 11 publications

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“…in watchlist applications but there are no studies that perform contact lens detection from images captured with iris on the move (IOM) systems. While NIR iris sensors are emerging in mobile devices, already the standard highquality cameras embedded in smartphones facilitate iris recognition [19]. Detection of print [27] and video [49] based presentation attacks from conventional RGB iris images has received already some attention but textured contact lenses have not been included in these preliminary works.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Contact lens detection in iris images

Komulainen¹,

Hadid²

2017

Iris and Periocular Biometric Recognition

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“…The motivation of this choice is the generality of the solution: it is applicable to several kinds of internet services. Moreover, while alternatives do exist in literature [16] - [21], CASHMA is one of the few approaches which realizes remote and transparent multibiometric authentication improving security of the user session without significantly reducing usability [38].…”

Section: The Cashma Approach For Continuous Multi-biometric Authenticmentioning

confidence: 99%

“…However, some critical ICT systems and applications need to be secured for the entire session, possibly without overly disturbing the user. Therefore, solutions based on biometric continuous authentication have been studied in literature [16]- [21]. Those approaches shift user identity verification from a single-occurrence to a continuous process; relatively recent solutions are able to transparently acquire biometric data to perform authentication without significantly reducing the system usability [3], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Continuous Biometric Verification for Non-Repudiation of Remote Services

Schiavone

Ceccarelli

Bondavalli

2017

Proceedings of the 12th International Conference on Availability, Reliability and Security

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As our society massively relies on ICT, security services are becoming essential to protect users and entities involved. Amongst such services, non-repudiation provides evidences of actions, protects against their denial, and helps solving disputes between parties. For example, it prevents denial of past behaviors as having sent or received messages. Noteworthy, if the information flow is continuous, evidences should be produced for the entirety of the flow and not only at specific points. Further, non-repudiation should be guaranteed by mechanisms that do not reduce the usability of the system or application. To meet these challenges, in this paper, we propose two solutions for non-repudiation of remote services based on multi-biometric continuous authentication. We present an application scenario that discusses how users and service providers are protected with such solutions. We also discuss the technological readiness of biometrics for non-repudiation services: the outcome is that, under specific assumptions, it is actually ready.

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“…The proposed method has better recognition rate than using SVM or Hamming distance alone. It is [20], have described FIRME (Face and Iris Recognition for Mobile Engagement) as a biometric application based on a multimodal recognition of face and iris, which is designed to be embedded in mobile devices. The starting one handles image acquisition.…”

Section: Literature Surveymentioning

confidence: 99%

Two Levels Fusion Based Multimodal Biometric Authentication Using Iris and Fingerprint Modalities

Sireesha¹,

Rani²,

Reddy³

et al. 2016

IJIES

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Abstract:In the document, we are intending to present an innovative technique for the multimodal biometric authentication. Initially the input image is preprocessed then offered to feature extraction, where the modified local binary pattern is effectively utilized. Thereafter, the extracted features are furnished to the feature level and score level fusions. In feature level fusion, extracted features are offered to the GSO where the optimal features are shortlisted, and are furnished to the optimized neural network which effectively detects the iris and fingerprint image. In score level fusion, extracted features from the iris image are offered to the PSO and naive bayes classifier here one score value is achieved. After that, extracted features from the fingerprint image are applied to the AGFS and then one score value is attained. Finally, both the score values are combined. The evaluation tools utilized precision, FAR and FRR. The proposed method implemented in MATLAB platform.

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FIRME: Face and Iris Recognition for Mobile Engagement

Cited by 131 publications

References 11 publications

Contact lens detection in iris images

Contact lens detection in iris images

Continuous Biometric Verification for Non-Repudiation of Remote Services

Two Levels Fusion Based Multimodal Biometric Authentication Using Iris and Fingerprint Modalities

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