Selection of parameters in iris recognition system

Marciniak, Tomasz; Dąbrowski, Adam; Chmielewska, Agata; Krzykowska, Agnieszka

doi:10.1007/s11042-012-1035-y

Cited by 16 publications

(7 citation statements)

References 14 publications

(10 reference statements)

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“…The presented software and hardware allow the effective implementation of the algorithms such as tracking, classification, and detection of objects in the real-time. This type of programming can be used to realize biometric techniques (e.g., face recognition or iris identification) [15].…”

Section: Discussionmentioning

confidence: 99%

Fast Prototyping for Video Monitoring Systems with the Use of DSP Module

Chmielewska¹,

Weychan²,

Marciniak³

et al. 2013

International Journal of Electronics and Telecommunications

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Abstract-This paper presents techniques for fast prototyping of real-time hardware / software video processing systems for urban surveillance monitoring equipment. During the experimental research the evaluation module with the TMS320DM6437 signal processor programmed with the use of the Code Composer Studio and Matlab / Simulink environments has been used. Analyzed algorithms can support the work of monitoring video operators. In particular, we analyzed efficiency of implementation of the algorithms using two examples: detection of painting theft and signaling of crossing a pedestrian pass at the red light.

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Section: Discussionmentioning

confidence: 99%

Fast Prototyping for Video Monitoring Systems with the Use of DSP Module

Chmielewska¹,

Weychan²,

Marciniak³

et al. 2013

International Journal of Electronics and Telecommunications

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“…The iris localization methods for the NIR images, available in the literature, localize the pupil using either the intensity thresholding based segmentation [12,17] or the edge detection based segmentation [8,18,19] techniques. In the edge detection and HT based methods, first an optimized edge-map of the iris image is created to reduce the false edges in the edge-map, so that the pupil boundary can be localized accurately using the CHT as described in [8,19]. The creation of the optimized edge-map of the iris image becomes more challenging if the images are noisy such as the CITHV4 database images.…”

Section: Related Workmentioning

confidence: 99%

“…One complex approach to create the optimal edge-map for the pupil localization in the noisy NIR images is described in [8]. The researchers have used either the techniques to get the optimal edge-map of the iris image [8,19] or the modified CHT algorithms [20][21][22] for localizing the pupils in the iris images. The complexity of algorithms increases in order to achieve high accuracy for noisy images, which in turn increases the computation time [11,13].…”

Section: Related Workmentioning

confidence: 99%

A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

Kumar

Asati

Gupta

2016

Journal of Electrical and Computer Engineering

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Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. The iris segmentation is a process of detecting the pupil, iris’s outer boundary, and eyelids in the iris image. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR) illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and occlusions by the eyelids, eyelashes, and eyebrow hair. In the proposed method, first, a novel edge-map is created from the iris image, which is based on combining the conventional thresholding and edge detection based segmentation techniques, and then, the general circular Hough transform (CHT) is used to find the pupil circle parameters in the edge-map. Our main contribution in this research is a novel edge-map creation technique, which reduces the false edges drastically in the edge-map of the iris image and makes the pupil localization in the noisy NIR images more accurate, fast, robust, and simple. The proposed method was tested with three iris databases: CASIA-Iris-Thousand (version 4.0), CASIA-Iris-Lamp (version 3.0), and MMU (version 2.0). The average accuracy of the proposed method is 99.72% and average time cost per image is 0.727 sec.

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“…The literature review reveals that the existing iris localization algorithms for the NIR images detect the pupil using either intensity thresholding [13], [14] or edge detection based segmentation techniques [7], [15], [16]. In the CHT based algorithms, first optimal edge maps of the iris image are generated that contain minimal false edges, so that the iris circles can be detected accurately and efficiently as demonstrated in [7] and [15].…”

Section: Introductionmentioning

confidence: 99%

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

Kumar¹,

Asati²,

Gupta³

2016

IJECE

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This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIAIris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results.

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Selection of parameters in iris recognition system

Cited by 16 publications

References 14 publications

Fast Prototyping for Video Monitoring Systems with the Use of DSP Module

Fast Prototyping for Video Monitoring Systems with the Use of DSP Module

A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

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