Improving colour iris segmentation using a model selection technique

Hu, Yang; Sirlantzis, Konstantinos; Howells, Gareth

doi:10.1016/j.patrec.2014.12.012

Cited by 38 publications

(19 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The idea of Hu et al [13] is to fuse different previously published iris segmentation techniques, selected according to their performance in particular cases of degraded images. They describe a model selection strategy, which selects the final iris parametrizations based on the candidates returned by the used baseline segmentation strategies.…”

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

View full text Add to dashboard Cite

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

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The images have resolution of 1536 × 2048, 2320 × 4128 and 640 × 480 pixels, respectively. We used the 569 ground truth masks made available by Hu et al [43] and labeled another 431 to complete 1,000 images from 75 subjects.…”

Section: A Datasetsmentioning

confidence: 99%

Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks

Bezerra

Laroca

Lucio

et al. 2018

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

View full text Add to dashboard Cite

The iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described. Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end a combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.

show abstract

“…They first used the relative total variation model (RTV) and then applied the Hough transform to detect the limbic and pupillary boundaries, and afterwards performed local grey-level analysis for pixel identification in order to segment the iris. Hu et al [23] employed HOG and a SVM to pick the best among the segmentation outcomes of three different segmentation algorithms (a circle model and two ellipse models). Another approach is that taken by Ouabida et al [24], who utilized the Optical Correlation based Active Contours (OCAC) to detect the iris and pupil contours.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

Fuentes-Hurtado

Naranjo

Diego-Más

et al. 2019

J Image Video Proc.

View full text Add to dashboard Cite

This work describes a new hybrid method for accurate iris segmentation from full-face images independently of the ethnicity of the subject. It is based on a combination of three methods: facial key-point detection, integro-differential operator (IDO) and mathematical morphology. First, facial landmarks are extracted by means of the Chehra algorithm in order to obtain the eye location. Then, the IDO is applied to the extracted sub-image containing only the eye in order to locate the iris. Once the iris is located, a series of mathematical morphological operations is performed in order to accurately segment it. Results are obtained and compared among four different ethnicities (Asian, Black, Latino and White) as well as with two other iris segmentation algorithms. In addition, robustness against rotation, blurring and noise is also assessed. Our method obtains state-of-the-art performance and shows itself robust with small amounts of blur, noise and/or rotation. Furthermore, it is fast, accurate, and its code is publicly available.

show abstract

Improving colour iris segmentation using a model selection technique

Cited by 38 publications

References 22 publications

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

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

Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks

A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

Contact Info

Product

Resources

About