A review of issues and challenges in designing Iris recognition Systems for noisy imaging environment

Hajari, Kamal; Bhoyar, Kishor K.

doi:10.1109/pervasive.2015.7087003

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…It therefore presents an enormous level of randomness. The iris recognition's key advantage, in addition, of the iris recognition speed during matching and its extreme resistance to false matches is the constancy of the iris as, protected, an internal but externally visible eye organ [6,7].…”

Section: Iris Recognitionmentioning

confidence: 99%

Conceptual Analysis of Iris Recognition Systems

Mohammed

Mawlood

2019

A.I.S.

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Ab s t r a c t. Biometric is a reliable measurable physical feature as passwords. A biometric framework automatically provides evidence of an individual's identity based on the individual's unique features. Iris recognition is one of the physiological ways used to identify a person in the group and is one of the accurate biometric systems. This paper deals briefly about the surveys those done before about Iris recognition system and its benefits and uses in information technology and security fields. The use of low-cost equipment can help to make iris recognition other standards in security contexts, as the requirements for a secure identification are walking. As human Iris gives a phenomenal design for identification. In recent year, the acquisition, rehabilitation, quality assessment, compression of the image, divisions, noise reduction, normalization, removal of features, iris code match, large-size data base searching, applications, evaluation, performance in different conditions, and multi biometrics have been developing irises recognition in several active fields of research. This research gives at the background of iris recognition and literature of methods in various fields. K e ywor d s : human Iris; biometric; security; authentication; Iris recognition.

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Section: Iris Recognitionmentioning

confidence: 99%

Conceptual Analysis of Iris Recognition Systems

Mohammed

Mawlood

2019

A.I.S.

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“…In addition, since the data processing of the CIS is also made up of a single bit without an ADC, it was expected that the operation speed (i.e., frames per second, fps) would be faster than that of the conventional image sensor and that the area and power consumption for the circuits would be smaller. It should be noted that the proposed CIS can employ the same processes of normalization, data encoding, and iris recognition as those of conventional iris algorithms [ 3 , 4 ].…”

Section: Design Of the Proposed Cis For Iris Segmentation Using Edmentioning

confidence: 99%

“…Iris recognition has been developing rapidly for unlocking the passcode of mobile phones [ 2 , 3 ]. Conventionally, in order to use the iris as a biometrics, an iris recognition algorithm must consist of image acquisition, pre-processing, iris image enhancement, binarization, and recognition processes as shown in Figure 1 [ 4 ]. A CMOS image sensor (CIS) plays an important role in capturing the iris images to be analyzed and converted.…”

Section: Introductionmentioning

confidence: 99%

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications

Park

Song

Kim

2018

Sensors

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This paper presents a single-bit CMOS image sensor (CIS) that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC) in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR) logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel) is 2.84 mm2 with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB) on an 8-bit ADC basis at a 50 MHz sampling frequency.

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“…On the other hand, in non-ideal environments, the images contain blurs, off-angles, non-uniform light intensities, and obstructions. Therefore, in both ideal and non-ideal environments, a real iris boundary without occlusion is required for better error-free features, so a segmentation algorithm is needed to separate each type of noise from the iris image and provide a real iris boundary even in non-ideal situations [ 13 ]. A good segmentation algorithm significantly affects the accuracy of the overall iris recognition system and can handle errors generated by occlusions of eyelashes, motion blurs, off-angle irises, specular reflections, standoff distances, eyeglasses, and poor illuminations [ 14 ].…”

Section: Introductionmentioning

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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A review of issues and challenges in designing Iris recognition Systems for noisy imaging environment

Cited by 20 publications

References 21 publications

Conceptual Analysis of Iris Recognition Systems

Conceptual Analysis of Iris Recognition Systems

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications

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

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