On iris camera interoperability

Arora, Sunpreet S.; Vatsa, Mayank; Singh, Richa; Jain, Anil K.

doi:10.1109/btas.2012.6374599

Cited by 27 publications

(7 citation statements)

References 12 publications

(12 reference statements)

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“…The increasing popularity of (peri-)ocular biometrics leads to a continuous development and upgrade of sensors, respectively. Differences among multiple types of sensors such as optical lens and illumination wavelength yield certain crosssensor variations which might lead to reduced biometric performance [19], [20]. A re-enrolment of subjects every time a new sensor is deployed is expensive and time-consuming, especially in large-scale applications.…”

Section: Introductionmentioning

confidence: 99%

Sex-Prediction from Periocular Images Across Multiple Sensors and Spectra

Tapia

Rathgeb

Busch

2018

2018 14th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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In this paper, we provide a comprehensive analysis of periocular-based sex-prediction (commonly referred to as gender classification) using state-of-the-art machine learning techniques. In order to reflect a more challenging scenario where periocular images are likely to be obtained from an unknown source, i.e. sensor, convolutional neural networks are trained on fused sets composed of several near-infrared (NIR) and visible wavelength (VW) image databases. In a crosssensor scenario within each spectrum an average classification accuracy of approximately 85% is achieved. When sex-prediction is performed across spectra an average classification accuracy of about 82% is obtained. Finally, a multi-spectral sex-prediction yields a classification accuracy of 83% on average. Compared to proposed works, obtained results provide a more realistic estimation of the feasibility to predict a subject's sex from the periocular region.

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Section: Introductionmentioning

confidence: 99%

Sex-Prediction from Periocular Images Across Multiple Sensors and Spectra

Tapia

Rathgeb

Busch

2018

2018 14th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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“…And, some methods have been proposed to overcome this problem, including a thin-plate spline calibration model [12], a decision tree based segmentation [4], a hierarchical registration algorithm [18] and so on. Besides, the problem of sensor interoperability in iris recognition was also concerned and many approaches have been proposed to mitigate the cross-sensor recognition performance degradation [14,2,10]. But, it is a pity that there is no literature exploring sensor interoperability in finger vein recognition.…”

Section: Introductionmentioning

confidence: 99%

Superpixel based finger vein ROI extraction with sensor interoperability

Yang

Zhou

Yin

2015

2015 International Conference on Biometrics (ICB)

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Finger vein is a new and promising trait in biometric recognition and some related progress have been achieved in recent years. Considering that there are many different sensors in a biometric system, sensor interoperability is a very important issue and still neglected in the state-ofthe-art finger vein recognition. Based on the analysis of the shortcomings in the current finger vein ROI extraction methods, this paper proposes a new superpixel based finger vein ROI extraction method with sensor interoperability. First, finger boundaries are determined by tracking superpixels which are very robust to image variations such as gray level and background noises. Furthermore, to handle finger displacement, the middle points of the detected finger boundaries are used to adjust finger direction. Finally, finger ROI is localized by the internal tangents of finger boundaries. Experimental results show that the proposed method can extract the ROIs accurately and adaptively from images which are captured by different sensors.978-1-4799-7824-3/15/$31.00

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“…number of learned filters). We used the set of filters 3 provided by the authors of [34]. The filters W i with different choices of parameter values were learned using 50000 patches that were randomly sampled from a set of 13 natural images provided in [33].…”

Section: Figure 17: Bsif Filters Size Of 7 × 7 Learned From Natural mentioning

confidence: 99%

“…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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On iris camera interoperability

Abstract: Abstract

Cited by 27 publications

References 12 publications

Sex-Prediction from Periocular Images Across Multiple Sensors and Spectra

Sex-Prediction from Periocular Images Across Multiple Sensors and Spectra

Superpixel based finger vein ROI extraction with sensor interoperability

Contact lens detection in iris images

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