26,163 research outputs found
Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition
Iris recognition algorithms, especially with the
emergence of large-scale iris-based identification systems, must
be tested for speed and accuracy and evaluated with a wide
range of templates – large size, long-range, visible and different
origins. This paper presents the acquisition of eye-iris images
of dark-skinned subjects in Africa, a predominant case of verydark-
brown iris images, under near-infrared illumination. The
peculiarity of these iris images is highlighted from the
histogram and normal probability distribution of their
grayscale image entropy (GiE) values, in comparison to Asian
and Caucasian iris images. The acquisition of eye-images for
the African iris dataset is ongoing and will be made publiclyavailable
as soon as it is sufficiently populated
Reducing false rejection rate in iris recognition by quality enhancement and information fusion
In this thesis we propose a set of algorithms to reduce the false rejection rate of iris recognition. Even though high recognition accuracy is claimed for iris recognition algorithms, high false rejection rates cause the impediment in worldwide use of iris biometrics.;A novel iris segmentation algorithm for non-ideal iris images treating iris as an elliptical object is proposed. Further, quality of the extracted iris image is improved using SVM based enhancement algorithm. In this algorithm, selected enhancement algorithms globally enhance the iris image and the learning algorithm synergistically fuses local information from these intermediate enhanced images. 1D log polar Gabor wavelet is then used to extract the textural features from the enhanced iris image and Euler numbers are used to extract the topological features. The extracted textural features give a global description of the iris image whereas the topological features are rotation, translation and scaling invariant. These two features are fused using the proposed match score and decision fusion algorithms. Among the three proposed fusion algorithm, SVM learning based match score fusion algorithm outperforms other fusion algorithms. Using CASIA, Miles, UBIRIS and UPOL iris databases, experimental results show that the proposed algorithm gives reduced failure to enroll rate with comparable accuracy
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Cross-eyed - cross-spectral iris/periocular recognition database and competition
This work presents a novel dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. This database was used in the 1st Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed 2016). This competition aimed at recording recent advances in cross- spectrum iris and periocular recognition. Six submissions were evaluated for cross-spectrum periocular recognition, and three for iris recognition. The submitted algorithms are briefly introduced. Detailed results are reported in this paper, and comparison of the results is discussed
Iris feature extraction: a survey
Biometric as a technology has been proved to be a reliable means of enforcing constraint in a security sensitiveenvironment. Among the biometric technologies, iris recognition system is highly accurate and reliable becauseof their stable characteristics throughout lifetime. Iris recognition is one of the biometric identification thatemploys pattern recognition technology with the use of high resolution camera. Iris recognition consist of manysections among which feature extraction is an important stage. Extraction of iris features is very important andmust be successfully carried out before iris signature is stored as a template. This paper gives a comprehensivereview of different fundamental iris feature extraction methods, and some other methods available in literatures.It also gives a summarised form of performance accuracy of available algorithms. This establishes a platform onwhich future research on iris feature extraction algorithm(s) as a component of iris recognition system can bebased.Keywords: biometric authentication, false acceptance rate (FAR), false rejection rate (FRR), feature extraction,iris recognition system
Feature Extraction From Epigenetic Traits Using Edge Detection In Iris Recognition System
Iris recognition is the most accurate biometric
identification system on hand. Most iris recognition systems use algorithms developed by Daugman. The performance of iris recognition is highly depends on edge detection. Canny is the edge detectors which commonly used. The objectives of this research are to a) study the edge detection criteria and b)measure the PSNR values in estimating the noise between the original iris feature and new iris template. The eye image with [320x280] dimension is obtained from the CASIA database which
has been pre-processed through the segmentation and
normalization in obtaining the rubber sheet model with [20x240] in dimension. Once it has been produced, the important information is extracted from the iris. Results show that, the PSNR values of iris feature before and after the process of extraction, was 24.93 and 9.12. For sobel and prewitt, both give 18.5 after the process. Based on our findings, the impact of edge detection techniques produces higher accuracy in iris recognition
system
An efficient iris image thresholding based on binarization threshold in black hole search method
In iris recognition system, the segmentation stage is one of the most important stages where the iris is located and then further segmented into outer and lower boundary of iris region. Several algorithms have been proposed in order to segment the outer and lower boundary of the iris region. The aim of this research is to identify the suitable threshold value in order to locate the outer and lower boundaries using Black Hole Search Method. We chose these methods because of the ineffient features of the other methods in image indetification and verifications. The experiment was conducted using three data set; UBIRIS, CASIA and MMU because of their superiority over others. Given that different iris databases have different file formats and quality, the images used for this work are jpeg and bmp. Based on the experimentation, most suitable threshold values for identification of iris aboundaries for different iris databases have been identified. It is therefore compared with the other methods used by other researchers and found out that the values of 0.3, 0.4 and 0.1 for database UBIRIS, CASIA and MMU respectively are more accurate and comprehensive. The study concludes that threshold values vary depending on the database
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