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- 摘要
- 关键词
- 实验方案
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[IEEE 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - Guangzhou, China (2018.10.8-2018.10.12)] 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - An Efficient Recognition Method for Incomplete Iris Image Based on CNN Model
摘要： The iris of the eye is a research hot spot in the field of biometric identification because of its uniqueness, non-contact and bioactivity. The incompleteness of the iris caused by the acquisition process has brought great uncertainty to the subsequent iris region segmentation and iris code matching, thereby reducing the efficiency of iris recognition. This paper describes a deep convolution neural network model with adaptive incomplete iris preprocessing mechanism. Based on the normalization of the iris image, the incomplete iris preprocessing mechanism adopts the method of making the inner circle or the outer circle. The iris region can be segmented by the line fitting and the circle fitting method for extracting as many iris features as possible. The deep convolution neural network then uses pixel coding of Irregular iris regions to complete the iris pattern classification. The model fully utilizes the characteristics of deep learning, local feature characterization and weight sharing, and realizes the problem of using large sample to compensate the incomplete feature of local feature. The experimental results show that this method has significant accuracy improvement compared with the traditional algorithms.
关键词： iris recognition,convolution neural network,iris image normalization,algorithm
更新于2025-09-23 15:23:52
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Biometric iris recognition using radial basis function neural network
摘要： The consistent and efficient method for the identification of biometrics is the iris recognition in view of the fact that it has richness in texture information. A good number of features performed in the past are built on handcrafted features. The proposed method is based on the feed-forward architecture and uses k-means clustering algorithm for the iris patterns classification. In this paper, segmentation of iris is performed using the circular Hough transform that realizes the iris boundaries in the eye and isolates the region of iris with no eyelashes and other constrictions. Moreover, Daugman's rubber sheet model is used to transform the resultant iris portion into polar coordinates in the process of normalization. A unique iris code is generated by log-Gabor filter to extract the features. The classification is achieved using neural network structures, the feed-forward neural network and the radial basis function neural network. The experiments have been conducted using the Chinese Academy of Sciences Institute of Automation (CASIA) iris database. The proposed system decreases computation time, size of the database and increases the recognition accuracy as compared to the existing algorithms.
关键词： Feed-forward neural network (FNN),Iris segmentation,Normalization,Biometrics,Radial basis function neural network (RBFNN),Iris recognition
更新于2025-09-23 15:23:52
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FRED-Net: Fully Residual Encoder-Decoder Network for Accurate Iris Segmentation
摘要： Iris recognition is now developed enough to recognize a person from a distance. The process of iris segmentation plays a vital role in maintaining the accuracy of the iris-based recognition systems by limiting the errors at the current stage. However, its performance is affected by non-ideal situations created by environmental light noise and user non-cooperation. The existing local feature-based segmentation methods are unable to find the true iris boundary in these non-ideal situations, and the error created at the segmentation stage traverses to all the subsequent stages, which results in reduced accuracy and reliability. In addition, it is necessary to segment the true iris boundary without the extra cost of denoising as preprocessing. To overcome these challenging issues during iris segmentation, a deep learning-based fully residual encoder-decoder network (FRED-Net) is proposed to determine the true iris region with the flow of high-frequency information from the preceding layers via residual skip connection. The main four impacts and significances of this study are as follows. First, FRED-Net is an end-to-end semantic segmentation network that does not use conventional image processing schemes, and does not have a preprocessing overhead. It is a standalone network in which eyelid, eyelash, and glint detections are not required to obtain the true iris boundary. Second, the proposed FRED-Net is the final resultant structure of a step-by-step development, and in each step, a new complete variant network is created for semantic segmentation considering the detailed descriptions of the networks. Third, FRED-Net uses the residual connectivity between convolutional layers by the residual shortcut for both encoder and decoder, which enables a high-frequency component to flow through the network and achieve higher accuracy with few layers. Fourth, the performance of the proposed FRED-Net is tested with five different iris datasets under visible and NIR light environments, and two general road scene segmentation datasets. To achieve fair comparisons with other studies, our trained FRED-Net models, along with the algorithms, are made publicly available through our website (Dongguk FRED-Net Model with Algorithm. accessed on 16 May 2018). The experiments include two datasets: Noisy Iris Challenge Evaluation - Part II (NICE-II) selected from the UBIRIS.v2 database and Mobile Iris Challenge Evaluation (MICHE-I), for the visible light environment and three datasets: Institute of Automation, Chinese Academy of Sciences (CASIA) v4.0 interval, v4.0 distance, and IIT Delhi v1.0, for the near-infrared (NIR) light environment. Moreover, to evaluate the performance of the proposed network in general segmentation, experiments with two famous road scene segmentation datasets: Cambridge-driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI), are included. The experimental results showed the optimum performance of the proposed FRED-Net on the above-mentioned seven datasets of iris and general road scene segmentation.
关键词： iris segmentation,full residual encoder-decoder network,Iris recognition,semantic segmentation
更新于2025-09-23 15:23:52
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[ACM Press the 2018 International Conference - Jeju, Republic of Korea (2018.04.27-2018.04.29)] Proceedings of the 2018 International Conference on Information Science and System - ICISS '18 - A Review on Iris Recognition in Non-Cooperative Environment
摘要： Nowadays, researcher is focus in developing reliable iris recognition systems for non-cooperative situations. The demand for iris recognition is increasing due to its reliability, accuracy and uniqueness. There are major factors involved in unconstrained environment such as obstruction by eyelids, eyelashes, glass frames, hair, off-angle, presence of contact lenses, poor illumination, motion blur, lighting and specular reflections, partially eye image, etc. The performance of the iris will be deteriorated and this results in lower recognition rate. In this paper, an overview of iris recognition for noisy imaging environments is presented included various related databases for iris recognition systems.
关键词： Biometrics,iris recognition,non-cooperative
更新于2025-09-23 15:22:29
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On the performance improvement of non-cooperative iris biometrics using segmentation and feature selection techniques
摘要： In this work, an improved segmentation methodology and a novel feature selection algorithm are proposed. From the input eye image, iris boundary is identified using Circular Hough Transform. A bounding box is defined using the radius obtained followed by iterative thresholding techniques to eliminate specular reflections, eyelids, eyelashes and pupil region. First-order and second-order statistical features are extracted from the segmented iris. For the first time, the statistical measure, Chi-square value is computed from GLCM as a new novel feature from iris images. Statistical dependency-based backward feature selection (SDBFS) algorithm is used to reduce the feature vector size. By operating on local features in reduced search space, computation complexity of segmentation is reduced with less mislocalisation count and eliminates pupil dilation effects. Results of SDBFS show the usefulness of minimal-useful features. Experimental results conducted on CASIA V1, V3-interval and UBIRIS V1 datasets show that statistical features in non-ideal iris images outperform some of the state-of-the-art methods.
关键词： backward feature selection,chi-square value,grey level co-occurrence matrix,iris recognition,GLCM,statistical dependency,Circular Hough Transform,segmentation
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM) - Shanghai, China (2018.11.24-2018.11.26)] 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM) - Image Preprocessing of Iris Recognition
摘要： The aim of this paper is to propose the methods for image preprocessing of image enhancement and boundary detection. Iris recognition has been widely considered as one of the most dependable identification method. However, the iris systems are still not widespread due to many factors, for example, the production cost, the processing time and the recognition rate. The problems of production cost and the processing time will be resolved with the development of integrate circuit technology. The problem of recognition rate mentioned here is not about the iris itself, but the acquisition of the effective image of the iris. The quality of the iris image has become the key point of the current iris system. The preprocessing of iris recognition involves hardware and software design of the system and in this paper both of the designs are discussed.
关键词： Hough transform,iris recognition,image preprocessing,histogram equalization
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE SENSORS - Montreal, QC, Canada (2019.10.27-2019.10.30)] 2019 IEEE SENSORS - Optical sensor based on pseudo-random diffractive optical elements for reliable gesture reconstruction
摘要： Biometric template aging is defined as an increase in recognition error rate with increased time since enrollment. It is believed that template aging does not occur for iris recognition. Several research groups, however, have recently reported experimental results showing that iris template aging does occur. This template aging effect manifests as a shift in the authentic distribution, resulting in an increased false non-match rate. Analyzing results from a three-year time-lapse data set, we find ～ 150% increase in the false non-match rate at a decision threshold representing a one in two million false match rate. We summarize several known elements of eye aging that could contribute to template aging, including age-related change in pupil dilation. Finally, we discuss various steps that can control the template aging effect in typical identity verification applications.
关键词： false non-match rate,iris recognition,Biometrics,error probability,template aging
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE International Symposium on Phased Array System & Technology (PAST) - Waltham, MA, USA (2019.10.15-2019.10.18)] 2019 IEEE International Symposium on Phased Array System & Technology (PAST) - Dual-Polarized 28-GHz Air-Filled SIW Phased Antenna Array for Next-Generation Cellular Systems
摘要： Biometric template aging is defined as an increase in recognition error rate with increased time since enrollment. It is believed that template aging does not occur for iris recognition. Several research groups, however, have recently reported experimental results showing that iris template aging does occur. This template aging effect manifests as a shift in the authentic distribution, resulting in an increased false non-match rate. Analyzing results from a three-year time-lapse data set, we find ～ 150% increase in the false non-match rate at a decision threshold representing a one in two million false match rate. We summarize several known elements of eye aging that could contribute to template aging, including age-related change in pupil dilation. Finally, we discuss various steps that can control the template aging effect in typical identity verification applications.
关键词： iris recognition,Biometrics,error probability,false non-match rate,template aging
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) - Helsinki, Finland (2019.7.22-2019.7.25)] 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) - A Deep Learning Method for Material Performance Recognition in Laser Additive Manufacturing
摘要： This paper presents a novel security architecture for protecting the integrity of iris images and templates using watermarking and visual cryptography (VC). The proposed scheme offers a complete protection framework for the iris biometrics which consists of two stages: the first stage is for iris image protection, while the second is for the iris template. First, for protecting the iris image, a watermark text which carries personal information is embedded in the middle band frequency region of the iris image using a novel watermarking algorithm that randomly interchanges multiple middle band pairs of the discrete cosine transform. Second, for iris template protection, the binary iris template is divided into two shares using VC, where one share is stored in the database and the other is kept with the user on a smart card. In addition, the SHA-2 hash function is utilized to maintain the integrity of the stored iris template in both the database and smart card. The experimental and comparison results on the CASIA V4 and UBIRIS V1 iris databases demonstrate that the proposed framework preserves the privacy of the iris images and templates and retains robustness to malicious attacks, while it does not have a discernible effect on the recognition performance.
关键词： security and privacy protection,iris recognition,smart card,visual cryptography,Biometrics,template security,watermarking
更新于2025-09-23 15:19:57
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Iris Recognition Using Gauss Laplace Filter
摘要： Biometrics deals with recognition of individuals based on their behavioral or biological features. The recognition of IRIS is one of the newer techniques of biometrics used for personal identification. It is one of the most widely used and reliable technique of biometrics. In this study a novel approach is presented for IRIS recognition. The proposed approach uses Gauss Laplace filter to recognize IRIS. The proposed approach decreases noise to the maximum extent possible, retrieves essential characteristics from image and matches those characteristics with data in a database. This method will be effective and simple and can be implemented in real time. The experiments are carried out using the images of IRIS acquired from a database and MATLAB application has been applied for its effective and simple manipulation of IRIS image. It was observed that developed approach has more accuracy and a relatively quicker time of execution than that of the existing approaches.
关键词： IRIS Recognition,Biometrics,Gauss Laplace Filter
更新于2025-09-19 17:15:36