A robust perception based method for iris tracking

Bruni, Vittoria; Vitulano, Domenico

doi:10.1016/j.patrec.2014.09.001

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…The most innovative point is to use the well known LBP texture descriptor scheme exclusively for the high frequency components of data, which is expected to improve the live/fake discriminability, when compared to the traditional use of this texture descriptor. Bruni and Vitulano [4] propose an application of the modified kernel object tracking to the specific problem of iris tracking. They rely on visual features of human irises that are instinctively used by human eye in the recognition process.…”

Section: Methods Participating In the Miche-i Challengementioning

confidence: 99%

Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation

Marsico

Nappi

Narducci

et al. 2018

Pattern Recognition

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Mobile biometrics technologies are nowadays the new frontier for secure use of data and services, and are considered particularly important due to the massive use of handheld devices in the entire world. Among the biometric traits with potential to be used in mobile settings, the iris/ocular region is a natural candidate, even considering that further advances in the technology are required to meet the operational requirements of such ambitious environments. Aiming at promoting these advances, we organized the Mobile Iris Challenge Evaluation (MICHE)-I contest. This paper presents a comparison of the performance of the participant methods by various Figures of Merit (FoMs). A particular attention is devoted to the identification of the image covariates that are likely to cause a decrease in the performance levels of the compared algorithms. Among these factors, interoperability among different devices plays an important role. The methods (or parts of them) implemented by the analyzed approaches are classified into segmentation (S), which was the main target of MICHE-I, and recognition (R). The paper reports both the results observed for either S or R, and also for different recombinations (S+R) of such methods. Last but not least, we also present the results obtained by multi-classifier strategies

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Section: Methods Participating In the Miche-i Challengementioning

confidence: 99%

Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation

Marsico

Nappi

Narducci

et al. 2018

Pattern Recognition

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“…Different from the previous studies in the literature, a new core-based iris detection algorithm was proposed in the last decade. 10 In addition, another method that is a watershed conversion and deep learning (DL)-based iris detection algorithm was introduced. 11 In addition to these, new approaches to iris detection that are mainly based on histogram smoothing, Canny edge detection, and Hough transformation were also proposed in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…9. Different from the previous studies in the literature, a new core-based iris detection algorithm was proposed in the last decade 10 . In addition, another method that is a watershed conversion and deep learning (DL)-based iris detection algorithm was introduced 11 .…”

Section: Introductionmentioning

confidence: 99%

Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

2022

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. Identification of people using different biometric data is becoming more important in network society. Biometrics include voice, ears, palms, fingerprints, faces, iris, retina, and hand shapes. Among these features, iris detection gets more attention because each iris type is unique and does not change throughout life. In this study, an iris recognition framework is proposed using deformable circular hollow kernels and uniform histogram fusion images (UHFIs). This system introduces two different approaches for the iris recognition: one with image matching without machine learning and the other one with machine learning. In the first approach, image matching through Gabor features is employed. After getting the circularly cropped UHFI, Gabor features are extracted and employed in the image matching-based recognition system. In addition, a normalized cross-correlation coefficient is used as a similarity metric to compare the Gabor feature vectors. In the second approach, Gabor feature images are extracted and employed in deep learning (DL). According to the experimental results, the proposed system reaches around 89% accuracy on MMU1, 86% accuracy on MMU2, and about 50% accuracy on the CASIAV3 and CASIAV4 datasets when no machine learning is employed. Extensive benchmarking results also indicate that the proposed system boosts the performance of conventional systems by at least around 40% in terms of accuracy in the absence of machine learning. However, in the presence of machine learning, experimental results show that the proposed method with DL achieves an accuracy of up to 100% for MMU1, MMU2, CASIAV3, and CASIAV4 datasets.

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