The Effects Of Gaze Angle And Iris Size On Off Angle Iris Recognition Using Deep Learning
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The Effects Of Gaze Angle And Iris Size On Off Angle Iris Recognition Using Deep Learning
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Author : Sydnee Phillips
language : en
Publisher:
Release Date : 2021
The Effects Of Gaze Angle And Iris Size On Off Angle Iris Recognition Using Deep Learning written by Sydnee Phillips and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Biometric identification categories.
Ai And Deep Learning In Biometric Security
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Author : Gaurav Jaswal
language : en
Publisher: CRC Press
Release Date : 2021-03-22
Ai And Deep Learning In Biometric Security written by Gaurav Jaswal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-22 with Technology & Engineering categories.
This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc. This text highlights a showcase of cutting-edge research on the use of convolution neural networks, autoencoders, recurrent convolutional neural networks in face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also provides a step-by-step guide to understanding deep learning concepts for biometrics authentication approaches and presents an analysis of biometric images under various environmental conditions. This book is sure to catch the attention of scholars, researchers, practitioners, and technology aspirants who are willing to research in the field of AI and biometric security.
Recognition Of Nonideal Iris Images Using Shape Guided Approach And Game Theory
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Author : Kaushik Roy
language : en
Publisher:
Release Date : 2011
Recognition Of Nonideal Iris Images Using Shape Guided Approach And Game Theory written by Kaushik Roy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The main objective of this thesis is to develop a nonideal iris recognition system by using active contour methods, Genetic Algorithms (GAs), shape guided model, Adaptive Asymmetrical Support Vector Machines (AASVMs) and Game Theory (GT). In this thesis, the proposed iris recognition method is divided into two phases: (1) cooperative iris recognition, and (2) noncooperative iris recognition. While most state-of-the-art iris recognition algorithms have focused on the preprocessing of iris images, recently, important new directions have been identified in iris biometrics research. These include optimal feature selection and iris pattern classification. In the first phase, we propose an iris recognition scheme based on GAs and asymmetrical SVMs. Instead of using the whole iris region, we elicit the iris information between the collarette and the pupil boundary to suppress the effects of eyelid and eyelash occlusions and to minimize the matching error. In the second phase, we process the nonideal iris images that are captured in unconstrained situations and those affected by several nonideal factors. The proposed noncooperative iris recognition method is further divided into three approaches. In the first approach of the second phase, we apply active contour-based curve evolution approaches to segment the inner/outer boundaries accurately from the nonideal iris images. The proposed active contour-based approaches show a reasonable performance when the iris/sclera boundary is separated by a blurred boundary. In the second approach, we describe a new iris segmentation scheme using GT to elicit iris/pupil boundary from a nonideal iris image. We apply a parallel game-theoretic decision making procedure by modifying Chakraborty and Duncan's algorithm to form a unified approach, which is robust to noise and poor localization and less affected by weak iris/sclera boundary. Finally, to further improve the segmentation performance, we propose a variational model to localize the iris region belonging to the given shape space using active contour method, a geometric shape prior and the Mumford-Shah functional. The verification and identification performance of the proposed scheme is validated using four challenging nonideal iris datasets, namely, the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and the WVU Nonideal, plus the non-homogeneous combined dataset. We have conducted several sets of experiments and finally, the proposed approach has achieved a Genuine Accept Rate (GAR) of 97.34% on the combined dataset at the fixed False Accept Rate (FAR) of 0.001% with an Equal Error Rate (EER) of 0.81%. The highest Correct Recognition Rate (CRR) obtained by the proposed iris recognition system is 97.39%.
Handbook Of Vascular Biometrics
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Author : Andreas Uhl
language : en
Publisher: Springer Nature
Release Date : 2020-01-01
Handbook Of Vascular Biometrics written by Andreas Uhl and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-01 with Biometric identification categories.
This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers.
Iris Recognition
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Author : Renu Sharma
language : en
Publisher:
Release Date : 2022
Iris Recognition written by Renu Sharma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Electronic dissertations categories.
Biometric systems recognize individuals based on their physical or behavioral traits, viz., face, iris, and voice. Iris (the colored annular region around the pupil) is one of the most popular biometric traits due to its uniqueness, accuracy, and stability. However, its widespread usage raises security concerns against various adversarial attacks. Another challenge is to match iris images with other compatible biometric modalities (i.e., face) to increase the scope of human identification. Therefore, the focus of this thesis is two-fold: firstly, enhance the security of the iris recognition system by detecting adversarial attacks, and secondly, accentuate its performance in iris-face matching.To enhance the security of the iris biometric system, we work over two types of adversarial attacks - presentation and morph attacks. A presentation attack (PA) occurs when an adversary presents a fake or altered biometric sample (plastic eye, cosmetic contact lens, etc.) to a biometric system to obfuscate their own identity or impersonate another identity. We propose three deep learning-based iris PA detection frameworks corresponding to three different imaging modalities, namely NIR spectrum, visible spectrum, and Optical Coherence Tomography (OCT) imaging inputting a NIR image, visible-spectrum video, and cross-sectional OCT image, respectively. The techniques perform effectively to detect known iris PAs as well as generalize well across unseen attacks, unseen sensors, and multiple datasets. We also presented the explainability and interpretability of the results from the techniques. Our other focuses are robustness analysis and continuous update (retraining) of the trained iris PA detection models. Another burgeoning security threat to biometric systems is morph attacks. A morph attack entails the generation of an image (morphed image) that embodies multiple different identities. Typically, a biometric image is associated with a single identity. In this work, we first demonstrate the vulnerability of iris recognition techniques to morph attacks and then develop techniques to detect the morphed iris images.The second focus of the thesis is to improve the performance of a cross-modal system where iris images are matched against face images. Cross-modality matching involves various challenges, such as cross-spectral, cross-resolution, cross-pose, and cross-temporal. To address these challenges, we extract common features present in both images using a multi-channel convolutional network and also generate synthetic data to augment insufficient training data using a dual-variational autoencoder framework. The two focus areas of this thesis improve the acceptance and widespread usage of the iris biometric system.
Face Expression And Iris Recognition Using Learning Based Approaches
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Author : Guodong Guo
language : en
Publisher:
Release Date : 2006
Face Expression And Iris Recognition Using Learning Based Approaches written by Guodong Guo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
Video Based Iris Feature Extraction And Matching Using Deep Learning
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Author : Anisia Jabin
language : en
Publisher:
Release Date : 2020
Video Based Iris Feature Extraction And Matching Using Deep Learning written by Anisia Jabin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Biometric identification categories.
"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.
Iris Recognition Using Support Vector Machines
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Author : Kaushik Roy
language : en
Publisher:
Release Date : 2006
Iris Recognition Using Support Vector Machines written by Kaushik Roy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
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.
A Study Of Segmentation And Normalization For Iris Recognition Systems
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Author : Ehsan Mohammadi Arvacheh
language : en
Publisher:
Release Date : 2006
A Study Of Segmentation And Normalization For Iris Recognition Systems written by Ehsan Mohammadi Arvacheh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
A Robust Method For Addressing Pupil Dilation In Iris Recognition
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Author : Raghunandan Pasula
language : en
Publisher:
Release Date : 2016
A Robust Method For Addressing Pupil Dilation In Iris Recognition written by Raghunandan Pasula and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Electronic dissertations categories.