Download or read book Machine Learning Based 3D Face Biometrics with Local Low level Geometrical Features written by Yinjie Lei and published by . This book was released on 2013 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: [Truncated abstract] Biometrics has been an active research area due to its enormous potential applications in video surveillance, human-machine interaction and access control systems. Among the biometric traits, the human face is the most publicly accepted biometric because of its non-intrusiveness and easy data acquisition. Most of the work on face recognition has been accomplished using 2D data. 2D face recognition systems are not robust to variations in pose, illumination conditions and facial expressions. With the rapid advancements in the development of data capturing technologies (e.g. Minolta Vivid and Microsoft Kinect), the acquisition of 3D data is becoming a more feasible task. 3D data processing has the potential to overcome the limitations and drawbacks faced by 2D facial data. Most of the existing 3D face recognition systems rely on the surface registration of the gallery and probe faces and/or on complex feature matching techniques. These methods are sensitive to facial expression and computationally expensive and are not suitable for real-world applications. In this thesis, we present novel algorithms based on low-level geometrical signatures which can be extracted at a low computational cost. To address the issue of facial expression variations, we adopt various machine learning techniques. This thesis is organized as a set of papers published in journals or currently under review. Three different local geometric feature based approaches have been proposed and their efficiency has been demonstrated through extensive experimental evaluations on the largest publicly available 3D face datasets. First, a fast and fully automatic approach based on four kinds of low-level geometrical features collected from the semi-rigid facial regions was devised and used to represent 3D faces. As a result, the effects of the deformed facial regions are avoided. The extracted features revealed to be efficient in computation and robust in the presence of facial expressions. A region-based histogram descriptor computed from these features was used as a single feature vector for a 3D face. The resulting feature vectors are independent of the coordinate system and hence can be tolerant to minor pose variations. A Support Vector Machine (SVM) was then trained as a classifier based on the proposed histogram descriptors to recognize any test face. In order to combine the contributions of the two semi-rigid facial regions (eyesforehead and nose), both feature-level and score-level fusion schemes are tested and compared. The experimental results demonstrate that feature-level fusion achieves a higher performance compared to score-level fusion. Second, in order to further increase the computational efficiency and robustness, a computationally efficient 3D face recognition approach is presented based on a novel facial signature called Angular Radial Signature (ARS). This approach extracts a set of ARS features from the semi-rigid regions of a 3D face. It was demonstrated that the extraction of these signatures is highly efficient (low computational cost). The Kernel Principal Component Analysis (KPCA) is subsequently used to extract the mid-level features from the ARSs to achieve a greater discriminative power and to deal with the linearly inseparable classification problem...

Download or read book Low level Fusion and Deformation Modeling for Textured 3D Face Biometrics written by Faisal Radhi M. Al-Osaimi and published by . This book was released on 2009 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: [Truncated abstract] Automatic face recognition has many crucial applications in a range of domains including human-machine interaction and security. The non-intrusive nature of face recognition is a key reason behind its suitability to such a broad range of applications. However, often these applications require high recognition accuracies and robustness which are challenging to achieve due to variations. Variations in pose, facial expression and illumination conditions may obscure the essential visual and/or geometric cues for recognition. The ability of a face recognition system to represent and match faces such that the utilized descriptive cues outweigh the associated variations is a key factor in enhancing its accuracy and robustness. This thesis presents novel algorithms that strive to achieve this goal by combining the descriptive cues in textured 3D facial scans at lower levels (data and feature fusion levels) and/or modeling the facial expression and illumination variations. An approach for combining fields of weak local and global geometrical cues (computed for each scan vertex) at the feature level was devised and used for compact representation and matching of 3D faces. The extracted fields are independent of the coordinate system in which the scan vertices were defined and hence the representation is tolerant to minor pose variations. The combined representation has demonstrated considerably high descriptiveness. In another presented approach, only local regions around key-points on the facial scans were considered. Heuristic measures for the descriptiveness of 3D local regions were defined and used for robust matching. The matching enforces consistent 3D rigid transformations and global structures among matched scans but on the other hand it allows for few mismatches among the local regions. Hence, it avoids the effects of deformed regions on matching. While the above strategies of combining information with and without the avoidance of the deformed regions gave a high recognition performance for scans under neutral expressions (minor deformations), the reported variation modeling approaches have shown higher recognition accuracies for faces with neutral or nonneutral expressions. The thesis also demonstrates that low level multimodal fusion in conjunction with variation modeling gives a higher performance than unimodal recognition and higher level fusion. Another contribution of this thesis is a novel approach for learning and morphing 3D expression deformations of 3D facial scans to those of target scans. This neutralizes the effects of expression variations among matched scans. In an extension of this approach, unseen facial scans under any facial expression were decomposed into estimates of neutral 3D faces and deformation residues. This is a very challenging problem due to the lack of the target scan. The estimated neutral faces and the deformations residues were used for face recognition and expression classification, respectively. The thesis also presents a novel approach for illumination normalization of the facial texture in textured scans...

Download or read book Face Biometrics for Personal Identification written by Besma Abidi and published by Springer Science & Business Media. This book was released on 2007-04-08 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides ample coverage of theoretical and experimental state-of-the-art work as well as new trends and directions in the biometrics field. It offers students and software engineers a thorough understanding of how some core low-level building blocks of a multi-biometric system are implemented. While this book covers a range of biometric traits, its main emphasis is placed on multi-sensory and multi-modal face biometrics algorithms and systems.

Download or read book Deep Learning in Biometrics written by Mayank Vatsa and published by CRC Press. This book was released on 2018-03-05 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning is now synonymous with applied machine learning. Many technology giants (e.g. Google, Microsoft, Apple, IBM) as well as start-ups are focusing on deep learning-based techniques for data analytics and artificial intelligence. This technology applies quite strongly to biometrics. This book covers topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoencoders. The focus is also on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints, while examining the future trends in deep learning and biometric research. Contains chapters written by authors who are leading researchers in biometrics. Presents a comprehensive overview on the internal mechanisms of deep learning. Discusses the latest developments in biometric research. Examines future trends in deep learning and biometric research. Provides extensive references at the end of each chapter to enhance further study.

Download or read book Learning 3D Geometric Features for Soft biometrics Recognition written by Baiqiang Xia and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soft-Biometric (gender, age, etc.) recognition has shown growingapplications in different domains. Previous 2D face based studies aresensitive to illumination and pose changes, and insufficient to representthe facial morphology. To overcome these problems, this thesis employsthe 3D face in Soft-Biometric recognition. Based on a Riemannian shapeanalysis of facial radial curves, four types of Dense Scalar Field (DSF) featuresare proposed, which represent the Averageness, the Symmetry, theglobal Spatiality and the local Gradient of 3D face. Experiments with RandomForest on the 3D FRGCv2 dataset demonstrate the effectiveness ofthe proposed features in Soft-Biometric recognition. Furtherly, we demonstratethe correlations of Soft-Biometrics are useful in the recognition. Tothe best of our knowledge, this is the first work which studies age estimation,and the correlations of Soft-Biometrics, using 3D face.

Download or read book Deep Learning for Biometrics written by Bir Bhanu and published by Springer. This book was released on 2017-08-01 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition; discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples; presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories. Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.

Download or read book Local Binary Pattern Network written by Meng Xi and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is well known as a method to extract hierarchical representations of data. This method has been widely implemented in many fields, including image classification, speech recognition, natural language processing, etc. Over the past decade, deep learning has made a great progress in solving face recognition problems due to its effectiveness. In this thesis a novel deep learning multilayer hierarchy based methodology, named Local Binary Pattern Network (LBPNet), is proposed. Unlike the shallow LBP method, LBPNet performs multi-scale analysis and gains high-level representations from low-level overlapped features in a systematic manner. The LBPNet deep learning network is generated by retaining the topology of Convolutional Neural Network (CNN) and replacing its trainable kernel with the off-the-shelf computer vision descriptor, the LBP descriptor. This enables LBPNet to achieve a high recognition accuracy without requiring costly model learning approach on massive data. LBPNet progressively extracts features from input images from test and training data through multiple processing layers, pairwisely measures the similarity of extracted features in regional level, and then performs the classification based on the aggregated similarity values. Through extensive numerical experiments using the popular benchmarks (i.e., FERET, LFW and YTF), LBPNet has shown the promising results. Its results out-perform (on FERET) or are comparable (on LFW and FERET) to other methods in the same categories, which are single descriptor based unsupervised learning methods on FERET and LFW, and single descriptor based supervise learning methods with image-restricted no outside data settings on LFW and YTF, respectively. --Leaves i-ii.

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.

Download or read book Handbook of Biometric Anti Spoofing written by Sébastien Marcel and published by Springer Nature. This book was released on 2023-02-23 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third edition of this authoritative and comprehensive handbook is the definitive work on the current state of the art of Biometric Presentation Attack Detection (PAD) – also known as Biometric Anti-Spoofing. Building on the success of the previous editions, this thoroughly updated third edition has been considerably revised to provide even greater coverage of PAD methods, spanning biometrics systems based on face, fingerprint, iris, voice, vein, and signature recognition. New material is also included on major PAD competitions, important databases for research, and on the impact of recent international legislation. Valuable insights are supplied by a selection of leading experts in the field, complete with results from reproducible research, supported by source code and further information available at an associated website. Topics and features: reviews the latest developments in PAD for fingerprint biometrics, covering recent technologies like Vision Transformers, and review of competition series; examines methods for PAD in iris recognition systems, the use of pupil size measurement or multiple spectra for this purpose; discusses advancements in PAD methods for face recognition-based biometrics, such as recent progress on detection of 3D facial masks and the use of multiple spectra with Deep Neural Networks; presents an analysis of PAD for automatic speaker recognition (ASV), including a study of the generalization to unseen attacks; describes the results yielded by key competitions on fingerprint liveness detection, iris liveness detection, and face anti-spoofing; provides analyses of PAD in finger-vein recognition, in signature biometrics, and in mobile biometrics; includes coverage of international standards in PAD and legal aspects of image manipulations like morphing.This text/reference is essential reading for anyone involved in biometric identity verification, be they students, researchers, practitioners, engineers, or technology consultants. Those new to the field will also benefit from a number of introductory chapters, outlining the basics for the most important biometrics. This text/reference is essential reading for anyone involved in biometric identity verification, be they students, researchers, practitioners, engineers, or technology consultants. Those new to the field will also benefit from a number of introductory chapters, outlining the basics for the most important biometrics.

Download or read book Machine Learning for Intelligent Multimedia Analytics written by Pardeep Kumar and published by Springer Nature. This book was released on 2021-01-16 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these large-scale data and also presents innovative solutions to address several potential research problems, e.g., enabling comprehensive visual classification to fill the semantic gap by exploring large-scale data, offering a promising frontier for detailed multimedia understanding, as well as extract patterns and making effective decisions by analyzing the large collection of data.

Download or read book Graph Based Methods in Computer Vision Developments and Applications written by Bai, Xiao and published by IGI Global. This book was released on 2012-07-31 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data. Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.

Download or read book AI and Deep Learning in Biometric Security written by Gaurav Jaswal and published by CRC Press. This book was released on 2021-03-21 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Object Recognition written by M. Bennamoun and published by Springer Science & Business Media. This book was released on 2001-12-12 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatie object recognition is a multidisciplinary research area using con cepts and tools from mathematics, computing, optics, psychology, pattern recognition, artificial intelligence and various other disciplines. The purpose of this research is to provide a set of coherent paradigms and algorithms for the purpose of designing systems that will ultimately emulate the functions performed by the Human Visual System (HVS). Hence, such systems should have the ability to recognise objects in two or three dimensions independently of their positions, orientations or scales in the image. The HVS is employed for tens of thousands of recognition events each day, ranging from navigation (through the recognition of landmarks or signs), right through to communication (through the recognition of characters or people themselves). Hence, the motivations behind the construction of recognition systems, which have the ability to function in the real world, is unquestionable and would serve industrial (e.g. quality control), military (e.g. automatie target recognition) and community needs (e.g. aiding the visually impaired). Scope, Content and Organisation of this Book This book provides a comprehensive, yet readable foundation to the field of object recognition from which research may be initiated or guided. It repre sents the culmination of research topics that I have either covered personally or in conjunction with my PhD students. These areas include image acqui sition, 3-D object reconstruction, object modelling, and the matching of ob jects, all of which are essential in the construction of an object recognition system.

Download or read book Proceedings of 3rd International Conference on Artificial Intelligence Advances and Applications written by Garima Mathur and published by Springer Nature. This book was released on 2023-04-14 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers outstanding research papers presented in the 3rd International Conference on Artificial Intelligence: Advances and Application (ICAIAA 2022), held in Poornima College of Engineering, Jaipur, India, during April 23–24, 2022. This book covers research works carried out by various students such as bachelor, master and doctoral scholars, faculty and industry persons in the area of artificial intelligence, machine learning, deep learning applications in health care, agriculture, and business, security. It also covers research in core concepts of computer networks, intelligent system design and deployment, real-time systems, WSN, sensors and sensor nodes, SDN, NFV, etc.

Download or read book 3D Facial Expressions Recognition Using Shape Analysis and Machine Learning written by Ahmed Maalej and published by . This book was released on 2012 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Facial expression recognition is a challenging task, which has received growing interest within the research community, impacting important applications in fields related to human machine interaction (HMI). Toward building human-like emotionally intelligent HMI devices, scientists are trying to include the essence of human emotional state in such systems. The recent development of 3D acquisition sensors has made 3D data more available, and this kind of data comes to alleviate the problems inherent in 2D data such as illumination, pose and scale variations as well as low resolution. Several 3D facial databases are publicly available for the researchers in the field of face and facial expression recognition to validate and evaluate their approaches. This thesis deals with facial expression recognition (FER) problem and proposes an approach based on shape analysis to handle both static and dynamic FER tasks. Our approach includes the following steps: first, a curve-based representation of the 3D face model is proposed to describe facial features. Then, once these curves are extracted, their shape information is quantified using a Riemannain framework. We end up with similarity scores between different facial local shapes constituting feature vectors associated with each facial surface. Afterwards, these features are used as entry parameters to some machine learning and classification algorithms to recognize expressions. Exhaustive experiments are derived to validate our approach and results are presented and compared to the related work achievements.

Download or read book Learning Techniques for Multi modal Facial Analysis written by Munawar Hayat and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Face and facial expression recognition are two important facial analysis tasks with numerous real life applications. This dissertation investigates the suitability of different data modalities for these two tasks. Specifically, the dissertation first proposes a method for the automatic analysis of textured 3D videos for facial expression recognition. The task of face recognition is then considered across multiple data modalities which include 3D static images and videos, RGB-D images acquired from low cost Kinect sensor and low quality grey scale images acquired from surveillance cameras. The dissertation is organized as a set of papers already published or submitted to journals or internationally refereed conferences. The dissertation first evaluates and compares existing methods of spatiotemporal feature description for 2D video-based facial expression recognition. It then presents an automatic framework, which exploits the dynamics of textured 3D videos for the recognizing six discrete facial expressions. Specifically, local video-patches of variable lengths are extracted from numerous locations of the training videos and represented as points on the Grassmannian manifold. An efficient graph-based spectral clustering algorithm is proposed to separately cluster these points for every expression class. Using a valid Grassmannian kernel function, the resulting cluster centers are embedded into a Reproducing Kernel Hilbert Space (RKHS) where six binary SVM models are learnt for classification. The dissertation then proposes manifold learning, deep learning and discriminative learning techniques for face recognition across multiple data modalities. First, a computationally efficient low level feature description method is proposed for face recognition from 3D static images. A method for the spatiotemporal evaluation of 3D videos is then presented. Face recognition from RGB-D images acquired from Kinect sensor is then considered as an image set classification problem. A method for the compact description of image sets using Riemannian geometry is proposed in this regards. For classification, SVM models are learnt on the Lie group of Riemannian manifold. The dissertation then finally considers face recognition from low quality imagery acquired from easily installable video surveillance cameras. Face recognition from this data modality is also studied under the framework of image set classification. For this purpose, two independent high performing methods are proposed. The first method learns deep reconstruction models, which can automatically discover the underlying complex geometric structure of the images in an image set. The second method empowers well developed binary classifiers for the task of multi-class image set classification. Compared to the existing binary to multiclass extension strategies, the proposed method is very efficient since it only trains few binary classifiers and uses very few images for the training of each of these classifiers.

Download or read book 3D Facial Recognition Using Local Geometric Features for Pose and Expression Variation written by Fatimah Khalid and published by . This book was released on 2008 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: