Automatic detection of non-cosmetic soft contact lenses in ocular images

Erdoğan, Gizem; Ross, Arun

doi:10.1117/12.2018096

Cited by 9 publications

(13 citation statements)

References 8 publications

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“…Their experiments suggested that even noncosmetic prescription contact lenses can impact the matching accuracy of iris recognition. Subsequent research by Erdogan and Ross [56] established the possibility of automatically detecting such lenses in iris images. Glass detection in face images was studied by Jiang et al in [114], [277], [272], with best results obtained using thermal images [101].…”

Section: A Materials Characteristicsmentioning

confidence: 99%

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Dantcheva

Elia

Ross

2016

IEEE Trans.Inform.Forensic Secur.

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338

224

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Recent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., "young Asian female with dark eyes and brown hair"). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

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Section: A Materials Characteristicsmentioning

confidence: 99%

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Dantcheva

Elia

Ross

2016

IEEE Trans.Inform.Forensic Secur.

Self Cite

338

224

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“…Algorithm for detection of soft contact and cosmetic contact lenses has been proposed [24]. The algorithm proposed in [7] is based on traditional edge detection exploiting sharp changes in pixel intensity.…”

Section: Related Literaturementioning

confidence: 99%

“…Detection of the presence of the contact lenses is the first step to improving the usability and reliability of iris recognition for contact lens wearers. One simple solution might be to change the decision threshold when a clear non-textured contact lens is detected such that the false non-match rate (FNMR) is identical to user who does not wear lenses [7], [8], [9], [10]. By wearing contact lenses, the accuracy of iris recognition was degraded.…”

Section: Introductionmentioning

confidence: 99%

Contact Lens Detection for Security

Cayabyab¹

2019

IJRASET

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This paper feature an efficient extraction and matching approach on eye with or without contact lens to innovate authentication process. The proposed feature extraction and matching approach is develop based on FGM (Feature Group Matching) which extract the key points and descriptor of each images using the two feature extraction algorithm which are SIFT (Scale-Invariant Feature Transform) and SURF(Speeded-Up Robust Features) and to match the key points and descriptors using FLANN based matcher. The major advantages of the developed approach are the simplicity and accuracy as compared to the prior extraction and matching approaches developed for contact lens detection images. The new used approach was tested in AMD Ryzen 5 2500U with 2GHz processor, 4GB Mobile RAM, dual graphic card and GPU rendering of RX 560 x with 5GB. The experimental results obtained from IIITD-Contact lens Iris Database and publicly available images and respectively achieved better performance in terms of simplicity and accuracy as compared to the previous proposed methods and Filipino Iris Database as an added contribution to the field of contact lens.

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“…Transparent (non-cosmetic) contact lenses can be considered to consist of two main categories based on the used material: rigid gas permeable (RGP) contact lenses and soft contact lenses [23]. The RGP lenses are well-known to degrade iris match quality because the whole lens fits within the iris region and thereby the boundary of the lens causes a severe circular artefact in the iris texture [5].…”

Section: Figure 12: Example Of Images Of Textured Lenses From the Ndmentioning

confidence: 99%

“…Erdogan and Ross [23] proposed a method for detecting non-cosmetic contact lenses in NIR ocular images by checking if the lens boundary can be found in the vicinity of the segmented limbic boundary. They reported moderate overall classification accuracies between 66.8% and 76.0% because inconsistent illumination and defocus blur lead to unsuccessful detections.…”

Section: Classification Of Contact Lens Typementioning

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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Automatic detection of non-cosmetic soft contact lenses in ocular images

Cited by 9 publications

References 8 publications

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Contact Lens Detection for Security

Contact lens detection in iris images

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