Human Identification by Partial Iris Segmentation Using Pupil Circle Growing Based on Binary Integrated Edge Intensity Curve

Patel, Hemal H.; Modi, Chintan K.; Paunwala, Mita; Patnaik, Suprava

doi:10.1109/csnt.2011.76

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Tables 3 and 4 shows GAR, FAR, FRR, EER and TER for the proposed method with and without normalization respectively. Boles et al [11] 24.68 Patel et al [22] 5.14 Yang et al [23] 3.01 Proposed method without normalization 7.03 Proposed method with normalization 2.21…”

Section: Ter ¼ Far þ Frrmentioning

confidence: 97%

Partial Segmentation and Matching Technique for Iris Recognition

Deshmukh

Prasad

2014

Computational Intelligence in Data Mining - Volume 1

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One of the main issues in iris segmentation is that the upper and lower region of the iris is occluded by the eyelashes and eyelids. In this paper, an effort has been made to solve the above problem. The pupil boundary is detected using adaptive thresholding and circular hough transform (CHT). The iris boundary is detected by drawing arcs of different radius from the pupil center and finding maximum change in intensity. The annular region between the iris inner and outer boundary is partially segmented. The segmented iris is transformed into adaptive rectangular size strip. Features are extracted using scale-invariant feature transform (SIFT). The experimental results show that the proposed technique has achieved high accuracy and low error rate.

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Section: Ter ¼ Far þ Frrmentioning

confidence: 97%

Partial Segmentation and Matching Technique for Iris Recognition

Deshmukh

Prasad

2014

Computational Intelligence in Data Mining - Volume 1

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“…Patel et al proposed a region growing of pupil circle and the method based on binary integrated curve of intensity to reduce the difficulties created by non-ideal segmentation conditions. The approach avoided the eyelid portion, and hence, was close to the real boundary [ 36 ]. Abate et al proposed an iris segmentation method for the images captured in visible light on mobile devices.…”

Section: Related Workmentioning

confidence: 99%

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

Arsalan

Naqvi

Kim

et al. 2018

Sensors

100

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The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

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“…Matching. The general iris recognition system consists of four important steps: (1) iris segmentation which extracts iris portion from the localized eye image, (2) iris normalization which converts the iris portion into rectangular strip of fixed dimensions to compensate for the deformation of pupil due to change in environmental conditions, (3) iris feature extraction deals with extraction of core iris features from the iris texture patterns and generate bitwise biometric template, and (4) iris template matching compares the stored template with the query template and gives the decision of authentication of a person based on some predefined threshold [19]. Among these steps the iris segmentation plays very important role in the whole system as it has to deal with eyelids and eyelashes occlusions, specular highlights.…”

Section: Iris Feature Extraction Andmentioning

confidence: 99%

DCT Watermarking Approach for Security Enhancement of Multimodal System

Paunwala¹,

Patnaik

2012

ISRN Signal Processing

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We have addressed a novel watermarking algorithm to support the capacity demanded by the multimodal biometric templates. Proposed technique embeds watermark in low frequency AC coefficients of selected 8 × 8 DCT blocks. Selection of blocks accomplishes perceptual transparency by exploiting the masking effects of human visual system (HVS). Embedding is done by modulating the coefficient magnitude as a function of its estimated value. Neighborhood estimation is used for the weighted DC coefficients from eight neighboring DCT blocks. The weights of the DC coefficients are calculated from local image intrinsic property. For our experimentation we have used iris and finger prints as the two templates which are watermarked into standard test images. The robustness of the proposed algorithm is compared with the few state-of-the-art literature when watermarked image is subjected to common channel attacks.

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Human Identification by Partial Iris Segmentation Using Pupil Circle Growing Based on Binary Integrated Edge Intensity Curve

Cited by 10 publications

References 10 publications

Partial Segmentation and Matching Technique for Iris Recognition

Partial Segmentation and Matching Technique for Iris Recognition

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

DCT Watermarking Approach for Security Enhancement of Multimodal System

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