Robust Video Based Iris Segmentation System in Less Constrained Environments

Mahadeo, Nitin K.; Paplinski, Andrew Peter; Ray, Sid

doi:10.1109/dicta.2013.6691524

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The dataset consists of videos of both right and left eyes. In this study, the iris regions were segmented using the segmentation algorithm developed for iris videos in less constrained environments [13]. The ground truth dataset refers to correct (+ve) and incorrect (-ve) segmentations determined by visual inspection.…”

Section: Methodsmentioning

confidence: 99%

Predicting segmentation errors in an iris recognition system

Mahadeo

Haffari

Paplinski

2015

2015 International Conference on Biometrics (ICB)

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Iris segmentation is defined as the isolation of the iris pattern in an eye image. A highly accurate segmented iris plays a key role in the overall performance of an iris recognition system, as shown in previous research. We present a fully automated method for classifying correctly and incorrectly segmented iris regions in eye images. In contrast with previous work where only iris boundary detection is considered (using a limited number of features), we introduce the following novelties which greatly enhance the performance of an iris recognition system. Firstly, we go beyond iris boundary detection and consider a more realistic and challenging task of complete segmentation which includes iris boundary detection and occlusion detection (due to eyelids and eyelashes). Secondly, an extended and rich feature set is investigated for this task. Thirdly, several non-linear learning algorithms are used to measure the prediction accuracy. Finally, we extend our model to iris videos, taking into account neighbouring frames for a better prediction. Both intrinsic and extrinsic evaluation are carried out to evaluate the performance of the proposed method. With these innovations, our method outperforms current state-ofthe-art techniques and presents a reliable approach to the task of classifying segmented iris images in an iris recognition system.

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Section: Methodsmentioning

confidence: 99%

Predicting segmentation errors in an iris recognition system

Mahadeo

Haffari

Paplinski

2015

2015 International Conference on Biometrics (ICB)

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“…The laboratory where the images were acquired in the present study had ordinary lighting with a series of fluorescent lamps but no dimmer switches or diffusers, and no other lighting was added. Thus, the proposed system is sufficiently robust to work in illumination varying environments that are influenced by surrounding light, as described in Mahadeo et al [19]. Our motivation was to overcome conventional recognition limitations, which demand image acquisition in controlled environments.…”

Section: Introductionmentioning

confidence: 97%

Robust computer vision system for marbling meat segmentation

Campos

Seixas

Barbon

et al. 2020

ELCVIA

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In this study, we developed a robust automatic computer vision system for marbling meat segmentation. Our approach can segment intramuscular fat from meat samples using images acquired with different quality devices in an illumination varying environment, where there was external ambient light and artificial light; thus, professionals can apply this method without specialized knowledge in terms of image treatment or equipment, as well as without disruption to normal procedures, thereby obtaining a robust solution. The proposed approach for marbling segmentation is based on data clustering and dynamic thresholding. Experiments were performed using two datasets that comprised 82 images of 41 longissimus dorsi muscles acquired by different sampling devices. The experimental results showed that the computer vision system performed well with over 98% accuracy and a low number of false positives, regardless of the acquisition device employed.

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“…For a given video, twenty-six frames of successfully segmented iris are selected, unwrapped and their respective iris codes are obtained by applying Log-Gabor filter on their iris patterns. An example of an iris region segmented using the method proposed in [15] is shown in Figure 1. The HD scores between each iris code and its remaining twenty-five counterparts are then computed as shown in expression (2).…”

Section: Consistent Vs Inconsistent Bitsmentioning

confidence: 99%