The Experts below are selected from a list of 93606 Experts worldwide ranked by ideXlab platform
Yingzi Du - One of the best experts on this subject based on the ideXlab platform.
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region based sift approach to Iris recognition
Optics and Lasers in Engineering, 2009Co-Authors: Craig Belcher, Yingzi DuAbstract:Traditional Iris recognition systems transfer Iris images to polar (or log-polar) coordinates and have performed very well on data that tends to have a centered gaze. The patterns of an Iris are part of a 3-D structure that is captured as a two-dimensional (2-D) image and cooperative Iris recognition systems are capable of correctly matching these 2-D representations of Iris features. However, when the gaze of an eye changes with respect to the camera lens, many times the size, shape, and detail of Iris patterns will change as well and cannot be matched to enrolled images using traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes much more challenging and noncooperative Iris algorithms will require a different approach. The direct application of the scale-invariant feature transform (SIFT) method would not work well for Iris recognition because it does not take advantage of the characteristics of Iris patterns. We propose the region-based SIFT approach to Iris recognition. This new method does not require polar transformation, affine transformation or highly accurate segmentation to perform Iris recognition and is scale invariant. This method was tested on the Iris challenge evaluation (ICE), WVU and IUPUI noncooperative databases and results show that the method is capable of cooperative and noncooperative Iris recognition.
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region based sift approach to Iris recognition
Optics and Lasers in Engineering, 2009Co-Authors: Craig Belcher, Yingzi DuAbstract:Traditional Iris recognition systems transfer Iris images to polar (or log-polar) coordinates and have performed very well on data that tends to have a centered gaze. The patterns of an Iris are part of a 3-D structure that is captured as a two-dimensional (2-D) image and cooperative Iris recognition systems are capable of correctly matching these 2-D representations of Iris features. However, when the gaze of an eye changes with respect to the camera lens, many times the size, shape, and detail of Iris patterns will change as well and cannot be matched to enrolled images using traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes much more challenging and noncooperative Iris algorithms will require a different approach. The direct application of the scale-invariant feature transform (SIFT) method would not work well for Iris recognition because it does not take advantage of the characteristics of Iris patterns. We propose the region-based SIFT approach to Iris recognition. This new method does not require polar transformation, affine transformation or highly accurate segmentation to perform Iris recognition and is scale invariant. This method was tested on the Iris challenge evaluation (ICE), WVU and IUPUI noncooperative databases and results show that the method is capable of cooperative and noncooperative Iris recognition.
David A Belyea - One of the best experts on this subject based on the ideXlab platform.
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peripheral laser iridoplasty opens angle in plateau Iris by thinning the cross sectional tissues
Clinical Ophthalmology, 2013Co-Authors: Tania Lamba, David A BelyeaAbstract:Plateau Iris syndrome has been described as persistent angle narrowing or occlusion with intraocular pressure elevation after peripheral iridotomy due to the abnormal plateau Iris configuration. Argon laser peripheral iridoplasty (ALPI) is an effective adjunct procedure to treat plateau Iris syndrome. Classic theory suggests that the laser causes the contraction of the far peripheral Iris stroma, “pulls” the Iris away from the angle, and relieves the Iris-angle apposition. We report a case of plateau Iris syndrome that was successfully treated with ALPI. Spectral domain optical coherence tomography confirmed the angle was open at areas with laser treatment but remained appositionally closed at untreated areas. Further analysis suggested significant cross-sectional thinning of the Iris at laser-treated areas in comparison with untreated areas. The findings indicate that APLI opens the angle, not only by contracting the Iris stroma, but also by thinning the Iris tissue at the crowded angle. This is consistent with the ALPI technique to aim at the Iris as far peripheral as possible. This case also suggests that spectral domain optical coherence tomography is a useful adjunct imaging tool to gonioscopy in assessing the angle condition.
Yunhong Wang - One of the best experts on this subject based on the ideXlab platform.
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improving Iris recognition accuracy via cascaded classifiers
Systems Man and Cybernetics, 2005Co-Authors: Yunhong WangAbstract:As a reliable approach to human identification, Iris recognition has received increasing attention in recent years. The most distinguishing feature of an Iris image comes from the fine spatial changes of the image structure. So Iris pattern representation must characterize the local intensity variations in Iris signals. However, the measurements from minutiae are easily affected by noise, such as occlusions by eyelids and eyelashes, Iris localization error, nonlinear Iris deformations, etc. This greatly limits the accuracy of Iris recognition systems. In this paper, an elastic Iris blob matching algorithm is proposed to overcome the limitations of local feature based classifiers (LFC). In addition, in order to recognize various Iris images efficiently a novel cascading scheme is proposed to combine the LFC and an Iris blob matcher. When the LFC is uncertain of its decision, poor quality Iris images are usually involved in intra-class comparison. Then the Iris blob matcher is resorted to determine the input Iris' identity because it is capable of recognizing noisy images. Extensive experimental results demonstrate that the cascaded classifiers significantly improve the system's accuracy with negligible extra computational cost.
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a fast and robust Iris localization method based on texture segmentation
Biometric Technology for Human Identification, 2004Co-Authors: Jiali Cui, Yunhong Wang, Tieniu Tan, Zhenan SunAbstract:With the development of the current networked society, personal identification based on biometrics has received more and more attention. Iris recognition has a satisfying performance due to its high reliability and non-invasion. In an Iris recognition system, preprocessing, especially Iris localization plays a very important role. The speed and performance of an Iris recognition system is crucial and it is limited by the results of Iris localization to a great extent. Iris localization includes finding the Iris boundaries (inner and outer) and the eyelids (lower and upper). In this paper, we propose an Iris localization algorithm based on texture segmentation. First, we use the information of low frequency of wavelet transform of the Iris image for pupil segmentation and localize the Iris with a differential integral operator. Then the upper eyelid edge is detected after eyelash is segmented. Finally, the lower eyelid is localized using parabolic curve fitting based on gray value segmentation. Extensive experimental results show that the algorithm has satisfying performance and good robustness.
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an Iris image synthesis method based on pca and super resolution
International Conference on Pattern Recognition, 2004Co-Authors: Jiali Cui, Yunhong Wang, Tieniu Tan, Junzhou Huang, Zhenan SunAbstract:It is very important for the performance evaluation of Iris recognition algorithms to construct very large Iris databases. However, limited by the real conditions, there are no very large common Iris databases now. In this paper, an Iris image synthesis method based on principal component analysis (PCA) and super-resolution is proposed. The Iris recognition algorithm based on PCA is first introduced and then, Iris image synthesis method is presented. The synthesis method first constructs coarse Iris images with the given coefficients. Then, synthesized Iris images are enhanced using super-resolution. Through controlling the coefficients, we can create many Iris images with specified classes. Extensive experiments show that the synthesized Iris images have satisfactory cluster and the synthesized Iris databases can be very large.
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robust encoding of local ordinal measures a general framework of Iris recognition
Lecture Notes in Computer Science, 2004Co-Authors: Yunhong WangAbstract:The randomness of Iris pattern makes it one of the most reliable biometric traits. On the other hand, the complex Iris image structure and various sources of intra-class variations result in the difficulty of Iris representation. Although diverse Iris recognition methods have been proposed, the fundamentals of Iris recognition have not a unified answer. As a breakthrough of this problem, we found that several accurate Iris recognition algorithms share a same idea — local ordinal encoding, which is the representation well-suited for Iris recognition. After further analysis and summarization, a general framework of Iris recognition is formulated in this paper. This work discovered the secret of Iris recognition. With the guidance of this framework, a novel Iris recognition method based on robust estimating the direction of image gradient vector is developed. Extensive experimental results demonstrate our idea.
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personal identification based on Iris texture analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003Co-Authors: Tieniu Tan, Yunhong Wang, Dexin ZhangAbstract:With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical Iris recognition system includes Iris imaging, Iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for Iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear Iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for Iris recognition, is then used to capture local characteristics of the Iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for Iris recognition is conducted on an Iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research.
Craig Belcher - One of the best experts on this subject based on the ideXlab platform.
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region based sift approach to Iris recognition
Optics and Lasers in Engineering, 2009Co-Authors: Craig Belcher, Yingzi DuAbstract:Traditional Iris recognition systems transfer Iris images to polar (or log-polar) coordinates and have performed very well on data that tends to have a centered gaze. The patterns of an Iris are part of a 3-D structure that is captured as a two-dimensional (2-D) image and cooperative Iris recognition systems are capable of correctly matching these 2-D representations of Iris features. However, when the gaze of an eye changes with respect to the camera lens, many times the size, shape, and detail of Iris patterns will change as well and cannot be matched to enrolled images using traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes much more challenging and noncooperative Iris algorithms will require a different approach. The direct application of the scale-invariant feature transform (SIFT) method would not work well for Iris recognition because it does not take advantage of the characteristics of Iris patterns. We propose the region-based SIFT approach to Iris recognition. This new method does not require polar transformation, affine transformation or highly accurate segmentation to perform Iris recognition and is scale invariant. This method was tested on the Iris challenge evaluation (ICE), WVU and IUPUI noncooperative databases and results show that the method is capable of cooperative and noncooperative Iris recognition.
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region based sift approach to Iris recognition
Optics and Lasers in Engineering, 2009Co-Authors: Craig Belcher, Yingzi DuAbstract:Traditional Iris recognition systems transfer Iris images to polar (or log-polar) coordinates and have performed very well on data that tends to have a centered gaze. The patterns of an Iris are part of a 3-D structure that is captured as a two-dimensional (2-D) image and cooperative Iris recognition systems are capable of correctly matching these 2-D representations of Iris features. However, when the gaze of an eye changes with respect to the camera lens, many times the size, shape, and detail of Iris patterns will change as well and cannot be matched to enrolled images using traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes much more challenging and noncooperative Iris algorithms will require a different approach. The direct application of the scale-invariant feature transform (SIFT) method would not work well for Iris recognition because it does not take advantage of the characteristics of Iris patterns. We propose the region-based SIFT approach to Iris recognition. This new method does not require polar transformation, affine transformation or highly accurate segmentation to perform Iris recognition and is scale invariant. This method was tested on the Iris challenge evaluation (ICE), WVU and IUPUI noncooperative databases and results show that the method is capable of cooperative and noncooperative Iris recognition.
Zhenan Sun - One of the best experts on this subject based on the ideXlab platform.
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towards complete and accurate Iris segmentation using deep multi task attention network for non cooperative Iris recognition
IEEE Transactions on Information Forensics and Security, 2020Co-Authors: Caiyong Wang, Yunlong Wang, Jawad Muhammad, Zhenan SunAbstract:Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing Iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based Iris segmentation approach, named IrisParseNet . Different from many previous CNN-based Iris segmentation methods, which only focus on predicting accurate Iris masks by following popular semantic segmentation frameworks, the proposed approach is a complete Iris segmentation solution, i.e., Iris mask and parameterized inner and outer Iris boundaries are jointly achieved by actively modeling them into a unified multi-task network. Moreover, an elaborately designed attention module is incorporated into it to improve the segmentation performance. To train and evaluate the proposed approach, we manually label three representative and challenging Iris databases, i.e., CASIA.v4-distance, UBIris.v2, and MICHE-I, which involve multiple illumination (NIR, VIS) and imaging sensors (long-range and mobile Iris cameras), along with various types of noises. Additionally, several unified evaluation protocols are built for fair comparisons. Extensive experiments are conducted on these newly annotated databases, and results show that the proposed approach achieves state-of-the-art performance on various benchmarks. Further, as a general drop-in replacement, the proposed Iris segmentation method can be used for any Iris recognition methodology, and would significantly improve the performance of non-cooperative Iris recognition.
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Iris image classification based on hierarchical visual codebook
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Co-Authors: Zhenan Sun, Hui Zhang, Tieniu Tan, Jianyu WangAbstract:Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each Iris image to a unique subject. In contrast, Iris image classification aims to classify an Iris image to an application specific category, e.g., Iris liveness detection (classification of genuine and fake Iris images), race classification (e.g., classification of Iris images of Asian and non-Asian subjects), coarse-to-fine Iris identification (classification of all Iris images in the central database into multiple categories). This paper proposes a general framework for Iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of Iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of Iris texture. Extensive experimental results demonstrate that the proposed Iris image classification method achieves state-of-the-art performance for Iris liveness detection, race classification, and coarse-to-fine Iris identification. A comprehensive fake Iris image database simulating four types of Iris spoof attacks is developed as the benchmark for research of Iris liveness detection.
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code level information fusion of low resolution Iris image sequences for personal identification at a distance
International Conference on Biometrics: Theory Applications and Systems, 2013Co-Authors: Jing Liu, Zhenan Sun, Tieniu TanAbstract:Iris images captured at a distance usually have low resolution (LR) Iris texture regions, which may lose some detailed identity information. The existed approaches try to improve the similarity of these LR Iris images to high resolution (HR) gallery samples through pixel-level or featurelevel super-resolution. We argue that binary codes of Iris feature templates are more directly relevant to Iris recognition performance. This paper proposes a code-level scheme for heterogeneous matching of LR and HR Iris images. The statistical relationship between a number of binary codes of LR Iris images and a binary code corresponding to the latent HR Iris image is established based on an adapted Markov network. Moreover, the cooccurence relationship between neighboring bits of HR Iris code is also modeled through this Markov network. So that we can obtain an enhanced Iris feature code from the probe set of LR Iris image sequences. In addition, a weight mask can also be derived from the Markov model, which can be used to further improve Iris recognition accuracy. Experimental results on Quality-Face/Iris Research Ensemble (Q-FIRE) database demonstrate that code-level information fusion performs significantly better than existed pixel-level, feature-level and score-level approaches for recognition of low resolution Iris image sequences.
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comprehensive assessment of Iris image quality
International Conference on Image Processing, 2011Co-Authors: Zhenan Sun, Tieniu TanAbstract:Iris image quality critically determines Iris recognition performance and the quality metrics of Iris images are also useful prior information for adaptive selection of optimal recognition strategy. Iris image quality is jointly determined by multiple factors such as focus, occlusion, off-angle, deformation, etc. So it is a complex problem to assess the overall quality score of an Iris image. This paper proposes a novel framework for comprehensive assessment of Iris image quality. The contributions of the paper include three aspects: (i) Three novel approaches are proposed to estimate the quality metrics (QM) of defocus, motion blur and off-angle in an Iris image respectively, (ii) A fusion method based on likelihood ratio is proposed to combine six quality factors of an Iris image into an unified quality score. (iii) A statistical quantization method based on t-test is proposed to adaptively classify the Iris images in a database into a number of quality levels. Extensive experiments demonstrate the proposed framework can effectively assess the overall quality of Iris images. And the relationship between Iris recognition results and the quality level of Iris images can be explicitly formulated.
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counterfeit Iris detection based on texture analysis
International Conference on Pattern Recognition, 2008Co-Authors: Zhuoshi Wei, Zhenan Sun, Xianchao Qiu, Tieniu TanAbstract:This paper addresses the issue of counterfeit Iris detection, which is a liveness detection problem in biometrics. Fake Iris mentioned here refers to Iris wearing color contact lens with textures printed onto them. We propose three measures to detect fake Iris: measuring Iris edge sharpness, applying Iris-Texton feature for characterizing the visual primitives of Iris textures and using selected features based on co-occurrence matrix (CM). Extensive testing is carried out on two datasets containing different types of contact lens with totally 640 fake Iris images, which demonstrates that Iris-Texton and CM features are effective and robust in anticounterfeit Iris. Detailed comparisons with two state-of-the-art methods are also presented, showing that the proposed Iris edge sharpness measure acquires a comparable performance with these two methods, while Iris-Texton and CM features outperform the state-of-the-art.
