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Iris And Periocular Recognition Using Deep Learning
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Book Synopsis Iris and Periocular Recognition using Deep Learning by : Ajay Kumar
Download or read book Iris and Periocular Recognition using Deep Learning written by Ajay Kumar and published by Elsevier. This book was released on 2024-06-28 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically explains the fundamental and most advanced techniques for ocular imprint-based human identification, with many applications in sectors such as healthcare, online education, e-business, metaverse, and entertainment. This is the first-ever book devoted to iris recognition that details cutting-edge techniques using deep neural networks. This book systematically introduces such algorithmic details with attractive illustrations, examples, experimental comparisons, and security analysis. It answers many fundamental questions about the most effective iris and periocular recognition techniques. ? Provides insightful algorithmic details into highly efficient and precise iris recognition using deep neural networks ? Unveils a collection of previously unpublished results and in-depth explanations of advanced ocular recognition algorithms ? Presents iris recognition algorithms specifically designed to bolster metaverse security, featuring specialized techniques for iris detection, segmentation, and matching ? Offers illustrative examples and comparative analysis, establishing reliability and confidence in deep learning-based methods over widely used conventional methods ? Provides access to the original codes and databases
Download or read book Iris Recognition written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2024-05-05 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Iris Recognition Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance. The discriminating powers of all biometric technologies depend on the amount of entropy they are able to encode and use in matching. Iris recognition is exceptional in this regard, enabling the avoidance of "collisions" even in cross-comparisons across massive populations. Its major limitation is that image acquisition from distances greater than a meter or two, or without cooperation, can be very difficult. However, the technology is in development and iris recognition can be accomplished from even up to 10 meters away or in a live camera feed. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Iris recognition Chapter 2: Retinal scan Chapter 3: John Daugman Chapter 4: Biometric points Chapter 5: Eye vein verification Chapter 6: Biometric device Chapter 7: Private biometrics Chapter 8: Aadhaar Chapter 9: Biometrics in schools Chapter 10: Aadhaar Act (II) Answering the public top questions about iris recognition. (III) Real world examples for the usage of iris recognition in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Iris Recognition.
Book Synopsis Swarm Intelligence for Iris Recognition by : Zaheera Zainal Abidin
Download or read book Swarm Intelligence for Iris Recognition written by Zaheera Zainal Abidin and published by CRC Press. This book was released on 2021-11-25 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Iris recognition is one of the highest accuracy techniques used in biometric systems. The accuracy of the iris recognition system is measured by False Reject Rate (FRR), which measures the authenticity of a user who is incorrectly rejected by the system due to changes in iris features (such as aging and health condition) and external factors that affect iris image, for instance, high noise rate. External factors such as technical fault, occlusion, and source of lighting that causes the image acquisition to produce distorted iris images create error, hence are incorrectly rejected by the biometric system. FRR can be reduced using wavelets and Gabor filters, cascaded classifiers, ordinal measures, multiple biometric modalities, and a selection of unique iris features. Nonetheless, in the long duration of the matching process, existing methods were unable to identify the authenticity of the user since the iris structure itself produces a template changed due to aging. In fact, the iris consists of unique features such as crypts, furrows, collarette, pigment blotches, freckles, and pupils that are distinguishable among humans. Earlier research was done by selecting unique iris features. However, these had low accuracy levels. A new way of identifying and matching the iris template using the nature-inspired algorithm is described in this book. It provides an overview of iris recognition that is based on nature-inspired environment technology. The book is useful for students from universities, polytechnics, community colleges; practitioners; and industry practitioners.
Book Synopsis The Effects of Gaze Angle and Iris Size on Off-angle Iris Recognition Using Deep Learning by : Sydnee Phillips
Download or read book The Effects of Gaze Angle and Iris Size on Off-angle Iris Recognition Using Deep Learning written by Sydnee Phillips and published by . This book was released on 2021 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning Efficient Deep Feature Extraction For Mobile Ocular Biometrics by : Sai Narsi Reddy Donthi Reddy
Download or read book Learning Efficient Deep Feature Extraction For Mobile Ocular Biometrics written by Sai Narsi Reddy Donthi Reddy and published by . This book was released on 2020 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ocular biometrics uses physical traits from eye regions such as iris, conjunctival vasculature, and periocular for recognizing the person. Ocular biometrics has gained popularity amongst research and industry alike for its identification capabilities, security, and simplicity in the acquisition, even using a mobile phone's selfie camera. With the rapid advancement in hardware and deep learning technologies, better performances have been obtained using Convolutional Neural Networks(CNN) for feature extraction and person recognition. Most of the early works proposed using large CNNs for ocular recognition in subject-dependent evaluation, where the subjects overlap between the training and testing set. This is difficult to scale for the large population as the CNN model needs to be re-trained every time a new subject is enrolled in the database. Also, many of the proposed CNN models are large, which renders them memory intensive and computationally costly to deploy on a mobile device. In this work, we propose CNN based robust subject-independent feature extraction for ocular biometric recognition, which is memory and computation efficient. We evaluated our proposed method on various ocular biometric datasets in the subject-independent, cross-dataset, and cross-illumination protocols.
Book Synopsis Human Recognition in Unconstrained Environments by : Maria De Marsico
Download or read book Human Recognition in Unconstrained Environments written by Maria De Marsico and published by Academic Press. This book was released on 2017-01-09 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human Recognition in Unconstrained Environments provides a unique picture of the complete ‘in-the-wild’ biometric recognition processing chain; from data acquisition through to detection, segmentation, encoding, and matching reactions against security incidents. Coverage includes: Data hardware architecture fundamentals Background subtraction of humans in outdoor scenes Camera synchronization Biometric traits: Real-time detection and data segmentation Biometric traits: Feature encoding / matching Fusion at different levels Reaction against security incidents Ethical issues in non-cooperative biometric recognition in public spaces With this book readers will learn how to: Use computer vision, pattern recognition and machine learning methods for biometric recognition in real-world, real-time settings, especially those related to forensics and security Choose the most suited biometric traits and recognition methods for uncontrolled settings Evaluate the performance of a biometric system on real world data Presents a complete picture of the biometric recognition processing chain, ranging from data acquisition to the reaction procedures against security incidents Provides specific requirements and issues behind each typical phase of the development of a robust biometric recognition system Includes a contextualization of the ethical/privacy issues behind the development of a covert recognition system which can be used for forensics and security activities
Book Synopsis Face Recognition Across the Imaging Spectrum by : Thirimachos Bourlai
Download or read book Face Recognition Across the Imaging Spectrum written by Thirimachos Bourlai and published by Springer. This book was released on 2016-02-12 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This authoritative text/reference presents a comprehensive review of algorithms and techniques for face recognition (FR), with an emphasis on systems that can be reliably used in operational environments. Insights are provided by an international team of pre-eminent experts into the processing of multispectral and hyperspectral face images captured under uncontrolled environments. These discussions cover a variety of imaging sensors ranging from state-of-the-art visible and infrared imaging sensors, to RGB-D and mobile phone image sensors. A range of different biometric modalities are also examined, including face, periocular and iris. This timely volume is a mine of useful information for researchers, practitioners and students involved in image processing, computer vision, biometrics and security.
Book Synopsis Face, Expression, and Iris Recognition Using Learning-based Approaches by : Guodong Guo
Download or read book Face, Expression, and Iris Recognition Using Learning-based Approaches written by Guodong Guo and published by . This book was released on 2006 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis PeriOcular Insights by : Sai Cherukuri
Download or read book PeriOcular Insights written by Sai Cherukuri and published by Independently Published. This book was released on 2024-04-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gender classification and age prediction are pivotal tasks in computer vision with profound implications. In this book, the author explores the challenges posed by factors like face masks, which obscure crucial facial features and introduces the "PeriGender" model. This model focuses on the periocular region around the eyes, leveraging deep learning techniques to achieve remarkable accuracy in gender classification. The author extends this work to age prediction, creating the "PeriAge" model, and combines both models to form the "PeriOcular" model, offering new insights and approaches for facial recognition systems. This book is a significant advancement in the field, providing a comprehensive understanding of these complex tasks and their practical applications.
Book Synopsis Deep Learning-Based Face Analytics by : Nalini K Ratha
Download or read book Deep Learning-Based Face Analytics written by Nalini K Ratha and published by Springer. This book was released on 2021-09-25 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field. Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition. This book is aimed at graduate students studying electrical engineering and/or computer science. Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra.
Book Synopsis Design and Implementation of Iris Pattern Recognition Based on Wireless Network Systems by : Thura Ali Khalaf
Download or read book Design and Implementation of Iris Pattern Recognition Based on Wireless Network Systems written by Thura Ali Khalaf and published by GRIN Verlag. This book was released on 2019-06-04 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2016 in the subject Computer Science - Technical Computer Science, grade: 81, , language: English, abstract: The goal of this thesis is to propose a fast and accurate iris pattern recognition system based on wireless network system. This thesis presents three parts; in the first part, Libor Masek algorithm is enhanced to achieve higher recognition rate. Another method of iris pattern recognition is proposed which named genetic algorithm. The two used iris pattern recognition methods are compared according to their accuracy and execution time. When testing persons of the Chinese Academy of Sciences Institute of Automation (CASIA) database, both methods achieved 100% recognition rates because there is at least one image sample for each person, which is correct matched and there is no person that is false matched. But when testing image samples per persons of CASIA database, the genetic algorithm achieved higher recognition rates and lower error rates than Libor Masek algorithm. It has been found, that the recognition time of genetic algorithm is less than Masek algorithm. The second part presents an iris image compression/decompression by using Principal Component Analysis (PCA) for compression process and Inverse Principal Component Analysis (IPCA) for decompression process. It has been proven that PCA is the most suitable method for compressing iris images because of its ability to reduce their size while maintaining the good quality of the reconstructed images. Reconstructed images using IPCA have low compression ratios (CRs) and high Peak to Signal Ratios (PSNRs), which leads to good quality. For more security, a multi-stage image compression is performed in order to protect network’s transmitted data from hackers because hackers cannot guess how much the image has been compressed. The third part, includes wireless network system consisting of one central Personal Computer (PC) and four Personal Computers (PCs) that communicate with each other through router device. The central PC takes the responsibility of monitoring and controlling the PCs of the whole network. All network PCs communicate with each other by using Transmission Control Protocol /Internet Protocol (TCP/IP) protocol suite that use client-server sockets to transfer images between PCs on the network.
Book Synopsis Face, Expression, and Iris Recognition Using Learning-based Approaches by : Guodong Guo
Download or read book Face, Expression, and Iris Recognition Using Learning-based Approaches written by Guodong Guo and published by VDM Publishing. This book was released on 2008-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates the problem of facial image analysis. Human faces contain a lot of information that is useful for many applications. For instance, the face and iris are important biometric features for security applications. Facial activity analysis such as face expression recognition is helpful for perceptual user interfaces. Developing new methods to improve recognition performance, in terms of face, iris, and facial expression, is a major concern in the presentation.
Book Synopsis Iris Recognition Using Artificial Neural Network by :
Download or read book Iris Recognition Using Artificial Neural Network written by and published by . This book was released on 2014 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Lris Segmentation, a Method of Biometric Recognition by : Leila Vajed
Download or read book Lris Segmentation, a Method of Biometric Recognition written by Leila Vajed and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A successful iris recognition process begins with iris segmentation. The segmentation of iris images under non-cooperative conditions is always challenging due to the extensive amount of noise in the images. The most successful iris segmentation methods are U-Net-based. There are limitations associated with this architecture, such as its lack of focus on target structures varying in shape, and its inability to segment objects whose shapes differ considerably. An attention residual encoding and decoding network is presented in this thesis for the purpose of developing an efficient deep learning-based method for segmenting the iris. With the proposed U-Net architecture, the multi-spatial feature attention and residual mechanisms are demonstrated to improve iris segmentation accuracy over previous convolutional neural network (CNN)-based methods. multi-spatial feature modules were included in the encoders and decoders of U-Net to find more prominent feature locations and prune feature outputs, so that only the information that needed to be preserved was maintained, and residual modules are used to improve segmentation robustness. Using this model, we can segment the iris effectively even when it is not cooperative. SVBPI (Sclera Blood Vessels, Periocular and Iris) dataset was used to train and evaluate the proposed method in order to compare the generalization and robustness of the method. The proposed U-Net architecture is a state-of-the-art approach to segmenting iris from the segmentation results.
Book Synopsis Video-based Iris Feature Extraction and Matching Using Deep Learning by : Anisia Jabin
Download or read book Video-based Iris Feature Extraction and Matching Using Deep Learning written by Anisia Jabin and published by . This book was released on 2020 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This research is initiated to enhance the video-based eye tracker’s performance to detect small eye movements.[1] Chaudhary and Pelz, 2019, created an excellent foundation on their motion tracking of iris features to detect small eye movements[1], where they successfully used the classical handcrafted feature extraction methods like Scale InvariantFeature Transform (SIFT) to match the features on iris image frames. They extracted features from the eye-tracking videos and then used patent [2] an approach of tracking the geometric median of the distribution. This patent [2] excludes outliers, and the velocity is approximated by scaling by the sampling rate. To detect the microsaccades (small, rapid eye movements that occur in only one eye at a time) thresholding was used to estimate the velocity in the following paper[1]. Our goal is to create a robust mathematical model to create a 2D feature distribution in the given patent [2]. In this regard, we worked in two steps. First, we studied a large number of multiple recent deep learning approaches along with the classical hand-crafted feature extractor like SIFT, to extract the features from the collected eye tracker videos from Multidisciplinary Vision Research Lab(MVRL) and then showed the best matching process for our given RIT-Eyes dataset[3]. The goal is to make the feature extraction as robust as possible. Secondly, we clearly showed that deep learning methods can detect more feature points from the iris images and that matching of the extracted features frame by frame is more accurate than the classical approach."--Abstract.
Book Synopsis Iris Recognition Using Support Vector Machines by : Kaushik Roy
Download or read book Iris Recognition Using Support Vector Machines written by Kaushik Roy and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, an iris recognition system is presented as a biometrically based technology for person identification using support vector machines (SVM). We propose two approaches for iris recognition, namely: The approach I, which is based on the whole information of iris region and the approach II, where only the zigzag collarette region is used for recognition. In approach I, Canny edge detection and Hough transform are used to find the iris/pupil boundary from eye's digital image. The rubber sheet model is applied to normalize the segmented iris image, Gabor wavelet technique is deployed to extract the deterministic features and the traditional SVM is used for iris patterns classification. In approach II, an iris recognition method is proposed using a novel iris segmentation scheme based on chain code and zigzag collarette area. The Multi-Objectives Genetic Algorithm (MOGA) is employed to select features extracted from the normalized collarette region by log-Gabor filters to increase the overall recognition accuracy. The traditional SVM is modified to asymmetrical SVM to treat False Accept and False Reject differently. Our experimental results indicate that the performance of SVM as a classifier is better than the performance of classifiers based on feed-forward neural network using backpropagation and Levenberg-Marquardt rule, K-nearest neighbor, and Hamming distance.
Book Synopsis New Matching Schemes for Iris Recognition by : Khaled Riad
Download or read book New Matching Schemes for Iris Recognition written by Khaled Riad and published by LAP Lambert Academic Publishing. This book was released on 2014-11-18 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biometric research has experienced significant advances in recent years given the need for more stringent security requirements. Iris recognition has been demonstrated to be an effcient and reliable technology for personal identification. In this book we employed three new matching schemes for iris recognition, the Scalar Product (SP), the Multi-dimensional Artificial Neural Networks (MDANN), and the Elastic Graph Matching (EGM). These three methods are trained and tested using two databases of gray scale eye images (CASIA and UBIRIS). They are trained using 996 and 723 iris images from the CASIA and UBIRIS database respectively. We have tested them using 915 and 448 iris images from the CASIA and UBIRIS database respectively. We have found that, there are 81 and 34 iris images from the CASIA and UBIRIS database respectively, are not used at all because of the failure analysis of locating iris for different causes. The Correct Recognition Rate (CCR) for the SP matching method is 98.26%, the CCR for the MDANN is 99.25%, and that for the EGM is 98.79%.
