Human eye localization using the modified Hough transform

Biometric information is widely used in user identification systems. Iris is one of the most reliable and accurate biometric information. In an iris recognition system, the iris localisation is one the most important parts because the performance of an iris recognition system is highly dependent on the accuracy of iris localisation. If unreliable iris regions are used in the iris recognition system, the recognition rate may be degraded. Therefore many researchers have studied the iris localisation methods. Especially, localising an iris region from noisy images is one of the hot topics in the iris recognition researches. In this study, the authors are concentrating on the iris localisation, where an efficient iris localisation method for noisy iris images is proposed. The proposed iris localisation method consists of two steps: pupil boundary localisation and iris boundary localisation. To localise a pupil region, an efficient block-based minimum energy detection method is used, in which specular reflection removal is performed as a preprocessing. Iris boundary is localised using a guided filter, the circular Hough transform and an ellipse fitting method. Experimental results with various test image sets show the effectiveness of the proposed method.

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“…Then, the circular Hough transform [13] and an ellipse fitting method [12] are used to localise an iris boundary.…”

Section: Proposed Iris Localisation Methodsmentioning

confidence: 99%

Efficient iris localisation using a guided filter

Han

Min

Park

2015

IET Image Processing

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“…The first one includes high resolution and texture rich color iris images, captured under visible lighting from 64 persons (3 for each eye) [19]. The second database contains greyscale images, collected from Asian persons [20].…”

Section: A Unimodal Biometrics Systems Based On Iris and Dorsalmentioning

confidence: 99%

Biometrics Recognition based on Image Local Features Ordinal Encoding

Emerich¹,

Belean²

2017

ijacsa

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Abstract-In the present informational era, with the continue extension of embedded computing systems, the demand of faster and robust image descriptors is an important issue. However, image representation and recognition is an open problem. The aim of the paper is to embrace ordinal measurements for image analysis and to apply the concept for a real problem, such as biometric identification. Biometrics provides a robust solution for the identity management process and is increasingly more present in our life. To explore the textural discriminative information of images, the paper proposes a new feature extraction technique, namely, Image Local Features Ordinal Encoding.

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“…There are many iris image databases freely such as CASIA, UBIRIS, MMU (Multimedia University, 2004), ICE (National Institute of Standards and Technology, 2006) and UPOL [8]. Here, the authors discussed CASIA and UBIRIS databases and describe the major characteristics of these common public and freely available noisy iris images databases (see Figure 2).…”

Section: Public Noisy Iris Image Databasesmentioning

confidence: 99%

Performance Evaluation of Proposed Segmentation Framework with Existing Techniques for Noisy Iris Images

Gupta¹,

Kumar²

2015

IJCA

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While different iris segmentation techniques continue to appear, there has been a lack of recognition accuracy of existing methods with noisy iris dataset. To handle iris images (captured on less constrained conditions) with some types of noise (iris obstructions and specular reflection), the authors shows the results of performance evaluation of their proposed iris segmentation technique over existing techniques. The performance of a proposed iris segmentation technique is evaluated based on the accuracy and time. To evaluate the performance, the authors use the most important points to compare their proposed technique with others, which is Equal Error Rate (EER). The system is implemented and tested using MATLAB Version 7.5.0.342 (R2007b) software. The environment where the experiments are performed in is Compaq PC, Core 2 Due Intel Pentium Processor (2.00 GHz), with 1GB RAM and Windows 7 operating system, a dataset of UBIRIS v1, UBIRIS v2 and CASIA-IrisV4 databases samples of iris data with different contrast quality.

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Human eye localization using the modified Hough transform

Cited by 87 publications

References 10 publications

Efficient iris localisation using a guided filter

Efficient iris localisation using a guided filter

Biometrics Recognition based on Image Local Features Ordinal Encoding

Performance Evaluation of Proposed Segmentation Framework with Existing Techniques for Noisy Iris Images

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