A robust segmentation approach to iris recognition based on video

Yu, Chen; Wang, Jin; Han, Changan; Wang, Lu; Adjouadi, Malek

doi:10.1109/aipr.2008.4906441

Cited by 31 publications

(6 citation statements)

References 8 publications

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“…With the increasing demands in public safety and security and with the necessity for critical/classified information access, secure identification is becoming a requisite that seeks high accuracy with a fast and reliable outcome. Thus, an effective iris recognition method is one that should initially overcome the rigid constraints imposed during iris image acquisition [8], and offer near real-time processing [9].…”

Section: Introductionmentioning

confidence: 99%

A highly accurate and computationally efficient approach for unconstrained iris segmentation

Chen

Adjouadi

Han

et al. 2010

Image and Vision Computing

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111

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Section: Introductionmentioning

confidence: 99%

A highly accurate and computationally efficient approach for unconstrained iris segmentation

Chen

Adjouadi

Han

et al. 2010

Image and Vision Computing

Self Cite

111

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“…On the other hand, outer boundary delimits the iris and the sclera. These two circles can be taken as two non concentric circles [9]. To define the each boundary and extract the iris region; the first step is to conduct edge detection to get the edge map of the eye images using a canny filter.…”

Section: Inner and Outer Boundary Detection Of An Irismentioning

confidence: 99%

Iris Segmentation Using a Statistical Approach

Fuentes

García-Vázquez

Ramírez-Acosta

2010

Advances in Pattern Recognition

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Abstract. Eyelashes and reflections occluding the iris region are noise factors that degrade the performance of iris recognition. If these factors are not eliminated in iris segmentation phase, they are incorrectly considered as the iris region. Thus, produce false iris pattern information which decreases the recognition rate. In this paper a statistical approach is used to improve iris segmentation phase eliminating this noise from none constrain images, which is composed in three parts, finding the pupil and limbus boundary, reflection detection and eyelash detection. First an edge map is calculated using canny filter then the Circular Hough Transform is used to improve circle parameter finding. An intensity variation analysis is use to recognize a strong reflection. Eyelashes are classified in two categories, separable and multiple. Intensity variances are used to detect multiple eyelashes and an edge detector to localize separable eyelashes. The results show that statistics are useful to decide when is necessary applied the eyelash detector.

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“…Several ophthalmologists notice during clinical research that the patterns on the iris are clearly distinctive and have unique texture potentially used successfully as a mean of biometric identification and it doesn't change during the lifespan of a person [1,2]. J. G. Daugman took advantage of the previous ideas and introduced the first and still the most widely used algorithm for iris recognition in 1993 [1].…”

Section: Introductionmentioning

confidence: 98%

Probability distribution function based iris recognition boosted by the mean rule

Pjatkin

Daneshmand

Rasti

2015

Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things

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In this work, a new iris recognition algorithm based on tonal distribution of iris images is introduced. During the process of identification probability distribution functions of colored irises are generated in HSI and YCbCr color spaces. The discrimination between classes is obtained by using KullbackLeibler divergence. In order to obtain the final decision on recognition, the multi decision on various color channels has been combined by employing mean rule. The decisions of H, S, Y, Cb and Cr color channels have been combined. The proposed technique overcome the conventional principle component analysis technique and achieved a recognition rate of 100% using the UPOL database. The major advantage is the fact that it is computationally less complex than the Daugman's algorithm and it is suitable for using visible light camera as opposed to the one proposed by Daugman where NIR cameras are used for obtaining the irises.

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A robust segmentation approach to iris recognition based on video

Cited by 31 publications

References 8 publications

A highly accurate and computationally efficient approach for unconstrained iris segmentation

A highly accurate and computationally efficient approach for unconstrained iris segmentation

Iris Segmentation Using a Statistical Approach

Probability distribution function based iris recognition boosted by the mean rule

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