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    Deep Learning for Biometrics [[electronic resource] /] : монография / ed.: Bhanu, Bir., Kumar, Ajay. - 1st ed. 2017. - [S. l. : s. n.]. - XXXI, 312 p. 117 illus., 96 illus. in color. - Б. ц.
    Зміст:
Part I: Deep Learning for Face Biometrics --
The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning --
Real-Time Face Identification via Multi-Convolutional Neural Network and Boosted Hashing Forest --
CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection --
Part II: Deep Learning for Fingerprint, Fingervein and Iris Recognition --
Latent Fingerprint Image Segmentation Using Deep Neural Networks --
Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing --
Iris Segmentation Using Fully Convolutional Encoder-Decoder Networks --
Part III: Deep Learning for Soft Biometrics --
Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style --
DeepGender2: A Generative Approach Toward Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Attention Shift Convolutional Neural Networks (PTAS-CNN) and Deep Convolutional Generative Adversarial Networks (DCGAN) --
Gender Classification from NIR Iris Images Using Deep Learning --
Deep Learning for Tattoo Recognition --
Part IV: Deep Learning for Biometric Security and Protection --
Learning Representations for Cryptographic Hash Based Face Template Protection --
Deep Triplet Embedding Representations for Liveness Detection.
Рубрики: Artificial intelligence.
   Biometrics (Biology).
   Computer science—Mathematics.
   Computer mathematics.
   Signal processing.
   Image processing.
   Speech processing systems.
   Artificial Intelligence.
   Biometrics.
   Mathematical Applications in Computer Science.
   Signal, Image and Speech Processing.
Анотація: 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. Dr. Bir Bhanu is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video. Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.

Перейти: https://doi.org/10.1007/978-3-319-61657-5

Дод.точки доступу:
Bhanu, Bir. \ed.\; Kumar, Ajay. \ed.\; SpringerLink (Online service)
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    Yan, Haibin.
    Facial Kinship Verification [[electronic resource] :] : a Machine Learning Approach / / Haibin. Yan, Lu, Jiwen. ; . - 1st ed. 2017. - [S. l. : s. n.]. - X, 82 p. 33 illus., 29 illus. in color. - Б. ц.
    Зміст:
1. Introduction to Facial Kinship Verification --
2. Feature Learning for Facial Kinship Verification --
3. Metric Learning for Facial Kinship Verification --
4. Video-Based Facial Kinship Verification --
5. Conclusions and Future Work.
Рубрики: Optical data processing.
   Pattern recognition.
   Biometrics (Biology).
   Image Processing and Computer Vision.
   Pattern Recognition.
   Biometrics.
Анотація: This book provides the first systematic study of facial kinship verification, a new research topic in biometrics. It presents three key aspects of facial kinship verification: 1) feature learning for kinship verification, 2) metric learning for kinship verification, and 3) video-based kinship verification, and reviews state-of-the-art research findings on facial kinship verification. Many of the feature-learning and metric-learning methods presented in this book can also be easily applied for other face analysis tasks, e.g., face recognition, facial expression recognition, facial age estimation and gender classification. Further, it is a valuable resource for researchers working on other computer vision and pattern recognition topics such as feature-learning-based and metric-learning-based visual analysis.

Перейти: https://doi.org/10.1007/978-981-10-4484-7

Дод.точки доступу:
Lu, Jiwen.; Yan, Haibin. \.\; SpringerLink (Online service)
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    Digital Communication. Towards a Smart and Secure Future Internet [[electronic resource] :] : 28th International Tyrrhenian Workshop, TIWDC 2017, Palermo, Italy, September 18-20, 2017, Proceedings / / ed.: Piva, Alessandro., Tinnirello, Ilenia., Morosi, Simone. - 1st ed. 2017. - [S. l. : s. n.]. - X, 263 p. 98 illus. - Б. ц.
    Зміст:
Biometric systems --
On the use of time information at long distance in biometric online signature recognition --
Automatic Face Recognition and Identification Tools in the Forensic Science Domain --
Biometric Fusion for Palm-Vein Based Recognition Systems --
Emerging services with NFV --
Availability Modeling and Evaluation of a Network Service Deployed via NFV --
Definition and Evaluation of Cold Migration Policies for the Minimization of the Energy Consumption in NFV Architectures --
A Lightweight Prediction Method for Scalable Analytics of Multi-Seasonal KPIs --
Multimedia forensics --
A copy-move detection algorithm based on geometric local binary pattern --
A Dataset for forensic analysis of videos in the wild --
Illumination Analysis in Physics-based Image Forensics: A Joint Discussion of Illumination Direction and Color --
Matrix Theory for Modeling the Eigenvalue Distribution of Images under Upscaling --
Security protocols --
A Security Evaluation of FIDO’s UAF Protocol in Mobile and Embedded Devices --
Delay Tolerant Revocation Scheme in Delay Tolerant VANETs --
Impact of Spreading Factor Imperfect Orthogonality in LoRa Communications --
Software Defined Networks --
A Network-Assisted Platform for Multipoint Remote Learning --
A de-verticalizing middleware for IoT systems based on Information Centric Networking design --
Technologies for IoT --
Measuring Spectrum Similarity in Distributed Radio Monitoring Systems --
Green and Heuristics-based Consolidation Scheme for Data Center Cloud Applications --
Implementing a per-flow Token Bucket using Open Packet Processor. .
Рубрики: Computer organization.
   Optical data processing.
   Application software.
   Data protection.
   Biometrics (Biology).
   Computer Systems Organization and Communication Networks.
   Image Processing and Computer Vision.
   Information Systems Applications (incl. Internet).
   Security.
   Biometrics.
Анотація: This book constitutes the proceedings of the 28th International Tyrrhenian Workshop on Digital Communication, TIWDC 2017, which took place in Palermo, Italy, in September 2017. The 18 papers presented in this volume were carefully reviewed and selected from 40 submissions. They were organized in topical sections named: biometric systems; emerging services with Network Function Virtualization (NFV); multimedia forensics; security protocols; software defined networks; and technologies for Internet of Things (IoT).

Перейти: https://doi.org/10.1007/978-3-319-67639-5

Дод.точки доступу:
Piva, Alessandro. \ed.\; Tinnirello, Ilenia. \ed.\; Morosi, Simone. \ed.\; SpringerLink (Online service)
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    Dasgupta, Dipankar.
    Advances in User Authentication [[electronic resource] /] : монография / Dipankar. Dasgupta, Roy, Arunava., Nag, Abhijit. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XIV, 360 p. 165 illus., 128 illus. in color. - Б. ц.
    Зміст:
Authentication Basics --
Biometric Authentication --
Negative Authentication Systems --
Pseudo-Passwords and Non-Textual Approaches --
Multi-Factor Authentication --
Continuous Authentication --
Adaptive Multi-Factor Authentication.
Рубрики: Coding theory.
   Information theory.
   Computer security.
   Data structures (Computer science).
   Biometrics (Biology).
   Coding and Information Theory.
   Systems and Data Security.
   Data Storage Representation.
   Biometrics.
Анотація: This book is dedicated to advances in the field of user authentication. The book covers detailed description of the authentication process as well as types of authentication modalities along with their several features (authentication factors). It discusses the use of these modalities in a time-varying operating environment, including factors such as devices, media and surrounding conditions, like light, noise, etc. The book is divided into several parts that cover descriptions of several biometric and non-biometric authentication modalities, single factor and multi-factor authentication systems (mainly, adaptive), negative authentication system, etc. Adaptive strategy ensures the incorporation of the existing environmental conditions on the selection of authentication factors and provides significant diversity in the selection process. The contents of this book will prove useful to practitioners, researchers and students. The book is suited to be used a text in advanced/graduate courses on User Authentication Modalities. It can also be used as a textbook for professional development and certification coursework for practicing engineers and computer scientists. .

Перейти: https://doi.org/10.1007/978-3-319-58808-7

Дод.точки доступу:
Roy, Arunava.; Nag, Abhijit.; Dasgupta, Dipankar. \.\; SpringerLink (Online service)
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    Zhang, David.
    Tongue Image Analysis [[electronic resource] /] : монография / David. Zhang, Zhang, Hongzhi., Zhang, Bob. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XV, 335 p. 193 illus., 144 illus. in color. - Б. ц.
    Зміст:
1. Introduction to Tongue Image Analysis --
2. Tongue Images Acquisition System Design --
3. Tongue Image Segmentation by Bi-Elliptical Deformable Contour --
4. A Snake-Based Approach to Automated Tongue Image Segmentation --
5. Tongue Segmentation in Hyperspectral Images --
6. Tongue Segmentation by Gradient Vector Flow and Region Merging --
7. Tongue Segmentation by Fusing Region-based and Edge-based Approaches --
8. Tongue Shape Classification by Geometric Features --
9. Color Correction Scheme for Tongue Images --
10. Tongue Color Checker for Precise Correction --
11. Tongue Color Analysis for Medical Application --
12. Statistical Analysis of Tongue Color and Its Applications in Diagnosis --
13. Hyperspetral Tongue Image Classification --
14. Computerized Tongue Diagnosis based on Bayesian Networks --
15. Tongue Image Analysis for Appendicitis Diagnosis --
16. Diagnosis Using Quantitative Tongue Feature Classification --
17. Detecting Diabetes Mellitus and Non-Proliferative Diabetic Retinopathy Using CTD Introduction --
18. Book Review and Future Word.
Рубрики: Biometrics (Biology).
   Optical data processing.
   Pattern recognition.
   Medicine, Chinese.
   Biomedical engineering.
   Health informatics.
   Biometrics.
   Image Processing and Computer Vision.
   Pattern Recognition.
   Traditional Chinese Medicine.
   Biomedical Engineering and Bioengineering.
   Health Informatics.
Анотація: This is the first book offering a systematic description of tongue image analysis and processing technologies and their typical applications in computerized tongue diagnostic (CTD) systems. It features the most current research findings in all aspects of tongue image acquisition, preprocessing, classification, and diagnostic support methodologies, from theoretical and algorithmic problems to prototype design and development of CTD systems. The book begins with a very in-depth description of CTD on a need-to-know basis which includes an overview of CTD systems and traditional Chinese medicine (TCM) in order to provide the information on the context and background of tongue image analysis. The core part then introduces algorithms as well as their implementation methods, at a know-how level, including image segmentation methods, chromatic correction, and classification of tongue images. Some clinical applications based on these methods are presented for the show-how purpose in the CTD research field. Case studies highlight different techniques that have been adopted to assist the visual inspection of appendicitis, diabetes, and other common diseases. Experimental results under different challenging clinical circumstances have demonstrated the superior performance of these techniques. In this book, the principles of tongue image analysis are illustrated with plentiful graphs, tables, and practical experiments to provide insights into some of the problems. In this way, readers can easily find a quick and systematic way through the complicated theories and they can later even extend their studies to special topics of interest. This book will be of benefit to researchers, professionals, and graduate students working in the field of computer vision, pattern recognition, clinical practice, and TCM, as well as those involved in interdisciplinary research.

Перейти: https://doi.org/10.1007/978-981-10-2167-1

Дод.точки доступу:
Zhang, Hongzhi.; Zhang, Bob.; Zhang, David. \.\; SpringerLink (Online service)
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    Kumar, Santosh.
    Animal Biometrics [[electronic resource] :] : techniques and Applications / / Santosh. Kumar, Singh, Sanjay Kumar., Singh, Rishav., Singh, Amit Kumar. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XXVIII, 243 p. 90 illus., 77 illus. in color. - Б. ц.
    Зміст:
Chapter 1. Introduction --
Chapter 2. Animal Biometric System --
Chapter 3. Animal Biometrics based Approaches --
Chapter 4. Classical Animal Recognition Methodology and Frameworks --
Chapter 5. Animal Biometrics based Recognition Systems (Based on Current State of the Art Approaches) --
Chapter 6. Representation and Identification of Species: Computer Vision and Pattern Recognition Models --
Chapter 7. Emerging Trends and Future Challenges. .
Рубрики: Pattern recognition.
   Optical data processing.
   Data encryption (Computer science).
   Biometrics (Biology).
   Pattern Recognition.
   Image Processing and Computer Vision.
   Cryptology.
   Biometrics.
Анотація: This book presents state-of-the-art methodologies and a comprehensive introduction to the recognition and representation of species and individual animals based on their physiological and phenotypic appearances, biometric characteristics, and morphological image patterns. It provides in-depth coverage of this emerging area, with an emphasis on the design and analysis techniques used in visual animal biometrics-based recognition systems. The book offers a comprehensive introduction to visual animal biometrics, addressing a range of recent advances and practices like sensing, feature extraction, feature selection and representation, matching, indexing of feature sets, and animal biometrics-based multimodal systems. It provides authoritative information on all the major concepts, as well as highly specific topics, e.g. the identification of cattle based on their muzzle point image pattern and face images to prevent false insurance claims, or the monitoring and registration of animals based on their biometric features. As such, the book provides a sound platform for understanding the Visual Animal Biometrics paradigm, a vital catalyst for researchers in the field, and a valuable guide for professionals. In addition, it can help both private and public organizations adapt and enhance their classical animal recognition systems.

Перейти: https://doi.org/10.1007/978-981-10-7956-6

Дод.точки доступу:
Singh, Sanjay Kumar.; Singh, Rishav.; Singh, Amit Kumar.; Kumar, Santosh. \.\; SpringerLink (Online service)
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    Handbook of Biometrics for Forensic Science [[electronic resource] /] : монография / ed.: Tistarelli, Massimo., Champod, Christophe. - 1st ed. 2017. - [S. l. : s. n.]. - VIII, 369 p. 139 illus., 89 illus. in color. - Б. ц.
    Зміст:
Biometric Technologies for Forensic Science and Policing: State of the Art --
Part I: Analysis of Fingerprints and Fingermarks --
Capture and Analysis of Latent Marks --
Automated Fingerprint Identification Systems: From Fingerprints to Fingermarks --
Challenges for Fingerprint Recognition: Spoofing, Skin Diseases and Environmental Effects --
Altered Fingerprint Detection --
Part II: Face and Video Analysis --
Face Sketch Recognition via Data-Driven Synthesis --
Recent Developments in Video-Based Face Recognition --
Face Recognition Technologies for Evidential Evaluation of Video Traces --
Human Factors in Forensic Face Identification --
Part III: Human Motion, Speech and Behavioral Analysis --
Biometric Evidence in Forensic Automatic Speaker Recognition --
On Using Soft Biometrics in Forensic Investigation --
Locating People in Surveillance Video Using Soft Biometric Traits --
Contact-Free Heartbeat Signal for Human Identification and Forensics --
Part IV: Statistical Analysis of Forensic Biometric Data --
From Biometric Scores to Forensic Likelihood Ratios --
Dynamic Signatures as Forensic Evidence: A New Expert Tool Including Population Statistics --
Part V: Ethical and Legal Issues --
Ethics and Policy of Forensic Biometrics.
Рубрики: Biometrics (Biology).
   Forensic science.
   Criminal law.
   Civil law.
   Biometrics.
   Forensic Science.
   Criminal Law and Criminal Procedure Law.
   Civil Law.
Анотація: This comprehensive handbook addresses the sophisticated forensic threats and challenges that have arisen in the modern digital age, and reviews the new computing solutions that have been proposed to tackle them. These include identity-related scenarios which cannot be solved with traditional approaches, such as attacks on security systems and the identification of abnormal/dangerous behaviors from remote cameras, for which advanced identification technologies and pattern recognition algorithms can offer novel ways to provide proof of identity. Topics and features: Presents the first dedicated volume on biometrics for forensic science and criminal investigations Contains contributions from an international selection of preeminent authorities, including members of the EU COST Action “Biometrics and Forensics for the Digital Age” Provides an in-depth analysis of the state of the art, together with a broad review of the available technologies and their potential applications Discusses potential future developments in the adoption of advanced technologies for the automated or semi-automated analysis of forensic traces Presents a particular focus on the acquisition and processing of data from real-world forensic cases Offers an holistic perspective, integrating work from different research institutions and combining viewpoints from both biometric technologies and forensic science This innovative volume will inspire and inform professionals, young researchers and graduate students interested in the use of cutting-edge biometric technologies in the service of criminal investigations. Dr. Massimo Tistarelli is a Professor of Computer Science in the Department of Communication Science and Information Technology at the University of Sassari. Dr. Christophe Champod is a Professor of Forensic Science in the School of Criminal Justice at the University of Lausanne.

Перейти: https://doi.org/10.1007/978-3-319-50673-9

Дод.точки доступу:
Tistarelli, Massimo. \ed.\; Champod, Christophe. \ed.\; SpringerLink (Online service)
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    Kavati, Ilaiah.
    Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems [[electronic resource] /] : монография / Ilaiah. Kavati, Prasad, Munaga V.N.K., Bhagvati, Chakravarthy. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XVII, 67 p. 29 illus. - Б. ц.
    Зміст:
Introduction --
Hierarchical Decomposition of Extended Triangulation for Fingerprint Indexing --
An Efficient Score-Based Indexing Technique for Fast Palmprint Retrieval --
A New Cluster-Based Indexing Technique for Palmprint Databases Using Scores and Decision-Level Fusion --
Conclusions and Future Scope.
Рубрики: Biometrics (Biology).
   Data protection.
   Information storage and retrieval.
   Special purpose computers.
   Biometrics.
   Security.
   Information Storage and Retrieval.
   Special Purpose and Application-Based Systems.
Анотація: This work presents a review of different indexing techniques designed to enhance the speed and efficiency of searches over large biometric databases. The coverage includes an extended Delaunay triangulation-based approach for fingerprint biometrics, involving a classification based on the type of minutiae at the vertices of each triangle. This classification is demonstrated to provide improved partitioning of the database, leading to a significant decrease in the number of potential matches during identification. This discussion is then followed by a description of a second indexing technique, which sorts biometric images based on match scores calculated against a set of pre-selected sample images, resulting in a rapid search regardless of the size of the database. The text also examines a novel clustering-based approach to indexing with decision-level fusion, using an adaptive clustering algorithm to compute a set of clusters represented by a ‘leader’ image, and then determining the index code from the set of leaders. This is shown to improve identification performance while using minimal resources.

Перейти: https://doi.org/10.1007/978-3-319-57660-2

Дод.точки доступу:
Prasad, Munaga V.N.K.; Bhagvati, Chakravarthy.; Kavati, Ilaiah. \.\; SpringerLink (Online service)
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    Biometric Recognition [[electronic resource] :] : 12th Chinese Conference, CCBR 2017, Shenzhen, China, October 28-29, 2017, Proceedings / / ed. Zhou, Jie. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XVII, 762 p. 352 illus. - Б. ц.
    Зміст:
Face --
Fingerprint --
Palm-print and vascular biometrics --
Iris --
Gesture and gait.-Emerging biometrics --
Voice and speech --
Video surveillance --
Feature extraction and classification theory --
Behavioral biometrics.
Рубрики: Biometrics (Biology).
   Pattern recognition.
   Optical data processing.
   Algorithms.
   Computer graphics.
   Application software.
   Biometrics.
   Pattern Recognition.
   Image Processing and Computer Vision.
   Algorithm Analysis and Problem Complexity.
   Computer Graphics.
   Information Systems Applications (incl. Internet).
Анотація: Recognition, CCBR 2017, held in Shenzhen, China, in October 2017. The 15 full papers and 65 poster papers presented in this book were carefully reviewed and selected from 138 submissions. The papers are organized in topical sections on face; fingerprint, palm-print and vascular biometrics; iris; gesture and gait; emerging biometrics; voice and speech; video surveillance; feature extraction and classification theory; behavioral biometrics.

Перейти: https://doi.org/10.1007/978-3-319-69923-3

Дод.точки доступу:
Zhou, Jie. \ed.\; Wang, Yunhong. \ed.\; Sun, Zhenan. \ed.\; Xu, Yong. \ed.\; Shen, Linlin. \ed.\; Feng, Jianjiang. \ed.\; Shan, Shiguang. \ed.\; Qiao, Yu. \ed.\; Guo, Zhenhua. \ed.\; Yu, Shiqi. \ed.\; SpringerLink (Online service)
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10.

    Pattern Recognition and Image Analysis [[electronic resource] :] : 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings / / ed.: Alexandre, Luis A., Salvador Sanchez, Jose., Rodrigues, Joao M. F. - 1st ed. 2017. - [S. l. : s. n.]. - XVI, 549 p. 180 illus. - Б. ц.
    Зміст:
Pattern Recognition and Machine Learning --
Computer Vision --
Image and Signal Processing --
Medical Image.-Applications.
Рубрики: Pattern recognition.
   Computer graphics.
   Optical data processing.
   Biometrics (Biology).
   Artificial intelligence.
   Pattern Recognition.
   Computer Graphics.
   Image Processing and Computer Vision.
   Biometrics.
   Artificial Intelligence.
Анотація: This book constitutes the refereed proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2017, held in Faro, Portugal, in June 2017. The 60 regular papers presented in this volume were carefully reviewed and selected from 86 submissions. They are organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Medical Image; and Applications.

Перейти: https://doi.org/10.1007/978-3-319-58838-4

Дод.точки доступу:
Alexandre, Luis A. \ed.\; Salvador Sanchez, Jose. \ed.\; Rodrigues, Joao M. F. \ed.\; SpringerLink (Online service)
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11.

    Foundations and Practice of Security [[electronic resource] :] : 9th International Symposium, FPS 2016, Quebec City, QC, Canada, October 24-25, 2016, Revised Selected Papers / / ed. Cuppens, Frederic. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XIII, 361 p. 83 illus. - Б. ц.
    Зміст:
Malware and Anomaly Detection --
Intrusion Response --
Vulnerability Analysis and Security Metrics --
Privacy and Verification --
Crypto and Communication Security --
Malware and Antivirus --
Web, Cloud, and Delegation --
Physical Security.
Рубрики: Data encryption (Computer science).
   Data protection.
   Coding theory.
   Information theory.
   Computer communication systems.
   Biometrics (Biology).
   Algorithms.
   Cryptology.
   Security.
   Coding and Information Theory.
   Computer Communication Networks.
   Biometrics.
   Algorithm Analysis and Problem Complexity.
Анотація: This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Symposium on Foundations and Practice of Security, FPS 2016, held in Quebec City, QC, Canada, in October 2016. The 18 revised regular papers presented together with 5 short papers and 3 invited talks were carefully reviewed and selected from 34 submissions. The accepted papers cover diverse research themes, ranging from classic topics, such as malware, anomaly detection, and privacy, to emerging issues, such as security and privacy in mobile computing and cloud.

Перейти: https://doi.org/10.1007/978-3-319-51966-1

Дод.точки доступу:
Cuppens, Frederic. \ed.\; Wang, Lingyu. \ed.\; Cuppens-Boulahia, Nora. \ed.\; Tawbi, Nadia. \ed.\; Garcia-Alfaro, Joaquin. \ed.\; SpringerLink (Online service)
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12.

    Image and Graphics [[electronic resource] :] : 9th International Conference, ICIG 2017, Shanghai, China, September 13-15, 2017, Revised Selected Papers, Part II / / ed.: Zhao, Yao., Kong, Xiangwei., Taubman, David. - 1st ed. 2017. - [S. l. : s. n.]. - XVII, 629 p. 268 illus. - Б. ц.
    Зміст:
Computer Vision and Pattern Recognition --
Compression, Transmission, Retrieval --
5G Multimedia Communications --
Artificial intelligence --
Biological and Medical Image Processing --
Color and Multispectral Processing --
Computational Imaging --
Computer Graphics and Visualization --
Hyperspectral Image Processing --
Multi-View and Stereoscopic Processing --
Representation, Analysis and Applications of Large-Scale 3D Multimedia Data --
Security --
Surveillance and remote sensing.
Рубрики: Optical data processing.
   Pattern recognition.
   Biometrics (Biology).
   Computer graphics.
   Artificial intelligence.
   Application software.
   Image Processing and Computer Vision.
   Pattern Recognition.
   Biometrics.
   Computer Graphics.
   Artificial Intelligence.
   Information Systems Applications (incl. Internet).
Анотація: This three-volume set LNCS 10666, 10667, and 10668 constitutes the refereed conference proceedings of the 9thInternational Conference on Image and Graphics, ICIG 2017, held in Shanghai, China, in September 2017. The 172 full papers were selected from 370 submissions and focus on advances of theory, techniques and algorithms as well as innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking.

Перейти: https://doi.org/10.1007/978-3-319-71589-6

Дод.точки доступу:
Zhao, Yao. \ed.\; Kong, Xiangwei. \ed.\; Taubman, David. \ed.\; SpringerLink (Online service)
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13.

    Image and Graphics [[electronic resource] :] : 9th International Conference, ICIG 2017, Shanghai, China, September 13-15, 2017, Revised Selected Papers, Part I / / ed.: Zhao, Yao., Kong, Xiangwei., Taubman, David. - 1st ed. 2017. - [S. l. : s. n.]. - XXX, 705 p. 334 illus. - Б. ц.
Рубрики: Optical data processing.
   Pattern recognition.
   Biometrics (Biology).
   Computer graphics.
   Artificial intelligence.
   Application software.
   Image Processing and Computer Vision.
   Pattern Recognition.
   Biometrics.
   Computer Graphics.
   Artificial Intelligence.
   Information Systems Applications (incl. Internet).
Анотація: This three-volume set LNCS 10666, 10667, and 10668 constitutes the refereed conference proceedings of the 9th International Conference on Image and Graphics, ICIG 2017, held in Shanghai, China, in September 2017. The 172 full papers were selected from 370 submissions and focus on advances of theory, techniques and algorithms as well as innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking.

Перейти: https://doi.org/10.1007/978-3-319-71607-7

Дод.точки доступу:
Zhao, Yao. \ed.\; Kong, Xiangwei. \ed.\; Taubman, David. \ed.\; SpringerLink (Online service)
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14.

    Transactions on Computational Science XXX [[electronic resource] :] : special Issue on Cyberworlds and Cybersecurity / / ed.: L. Gavrilova, Marina., Tan, C.J. Kenneth., Sourin, Alexei. - 1st ed. 2017. - [S. l. : s. n.]. - XIII, 111 p. 59 illus. - Б. ц.
    Зміст:
Emerging Directions in Virtual Worlds and Biometric Security Research --
KINECT Face Recognition Using Occluded Area Localization Method --
Scene-Aware Style Transferring Using GIST --
Privacy-Preserved Spatial Skyline Queries in Location-Based Services --
Comparison Analysis of Overt and Covert Mental Stimuli of Brain Signal for Person Identification --
The Man-Machine Finger-Guessing Game Based on Cooperation Mechanism.
Рубрики: Computer security.
   Optical data processing.
   Biometrics (Biology).
   Computer graphics.
   Computers and civilization.
   User interfaces (Computer systems).
   Systems and Data Security.
   Image Processing and Computer Vision.
   Biometrics.
   Computer Graphics.
   Computers and Society.
   User Interfaces and Human Computer Interaction.
Анотація: The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings, and solutions, and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods. This, the 30th issue of the Transactions on Computational Science journal, is comprised of extended versions of selected papers from the International Conference on Cyberworlds, held in Chongqing, China, in September 2016. The first paper is a position paper giving an outline of current research at the intersection of cybersecurity and cyberworlds, and specifically focusing on mining behavioral data from online social networks. The remaining 5 papers focus on a range of topics, including privacy assurance in online location services, human gait recognition using KINECT sensors, hand-gesture recognition for computer games, scene matching between the source image and the target image for virtual reality applications, and human identification using brain waves.

Перейти: https://doi.org/10.1007/978-3-662-56006-8

Дод.точки доступу:
L. Gavrilova, Marina. \ed.\; Tan, C.J. Kenneth. \ed.\; Sourin, Alexei. \ed.\; SpringerLink (Online service)
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15.

    Pattern Recognition and Machine Intelligence [[electronic resource] :] : 7th International Conference, PReMI 2017, Kolkata, India, December 5-8, 2017, Proceedings / / ed. Shankar, B. Uma. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XXVIII, 695 p. 238 illus. - Б. ц.
    Зміст:
Pattern recognition and machine learning --
Signal and image processing --
Computer vision and video processing --
Soft and natural computing --
Speech and natural language processing --
Bioinformatics and computational biology --
Data mining and big data analytics --
Deep learning --
Spatial data science and engineering --
Applications of pattern recognition and machine intelligence.
Рубрики: Special purpose computers.
   Artificial intelligence.
   Natural language processing (Computer science).
   Optical data processing.
   Biometrics (Biology).
   Special Purpose and Application-Based Systems.
   Artificial Intelligence.
   Natural Language Processing (NLP).
   Natural Language Processing (NLP).
   Image Processing and Computer Vision.
   Biometrics.
Анотація: This book constitutes the proceedings of the 7th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2017,held in Kolkata, India, in December 2017. The total of 86 full papers presented in this volume were carefully reviewed and selected from 293 submissions. They were organized in topical sections named: pattern recognition and machine learning; signal and image processing; computer vision and video processing; soft and natural computing; speech and natural language processing; bioinformatics and computational biology; data mining and big data analytics; deep learning; spatial data science and engineering; and applications of pattern recognition and machine intelligence.

Перейти: https://doi.org/10.1007/978-3-319-69900-4

Дод.точки доступу:
Shankar, B. Uma. \ed.\; Ghosh, Kuntal. \ed.\; Mandal, Deba Prasad. \ed.\; Ray, Shubhra Sankar. \ed.\; Zhang, David. \ed.\; Pal, Sankar K. \ed.\; SpringerLink (Online service)
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16.

    Image and Graphics [[electronic resource] :] : 9th International Conference, ICIG 2017, Shanghai, China, September 13-15, 2017, Revised Selected Papers, Part III / / ed.: Zhao, Yao., Kong, Xiangwei., Taubman, David. - 1st ed. 2017. - [S. l. : s. n.]. - XVIII, 675 p. 344 illus. - Б. ц.
    Зміст:
Computer Vision and Pattern Recognition --
Compression, Transmission, Retrieval --
5G Multimedia Communications --
Artificial intelligence --
Biological and Medical Image Processing --
Color and Multispectral Processing --
Computational Imaging --
Computer Graphics and Visualization --
Hyperspectral Image Processing --
Multi-View and Stereoscopic Processing --
Representation, Analysis and Applications of Large-Scale 3D Multimedia Data --
Security --
Surveillance and remote sensing. .
Рубрики: Optical data processing.
   Pattern recognition.
   Biometrics (Biology).
   Computer graphics.
   Artificial intelligence.
   Application software.
   Image Processing and Computer Vision.
   Pattern Recognition.
   Biometrics.
   Computer Graphics.
   Artificial Intelligence.
   Information Systems Applications (incl. Internet).
Анотація: This three-volume set LNCS 10666, 10667, and 10668 constitutes the refereed conference proceedings of the 9th International Conference on Image and Graphics, ICIG 2017, held in Shanghai, China, in September 2017. The 172 full papers were selected from 370 submissions and focus on advances of theory, techniques and algorithms as well as innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking.

Перейти: https://doi.org/10.1007/978-3-319-71598-8

Дод.точки доступу:
Zhao, Yao. \ed.\; Kong, Xiangwei. \ed.\; Taubman, David. \ed.\; SpringerLink (Online service)
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17.

    Mining Intelligence and Knowledge Exploration [[electronic resource] :] : 5th International Conference, MIKE 2017, Hyderabad, India, December 13–15, 2017, Proceedings / / ed.: Ghosh, Ashish., Pal, Rajarshi., Prasath, Rajendra. - 1st ed. 2017. - [S. l. : s. n.]. - XX, 438 p. 125 illus. - Б. ц.
Рубрики: Artificial intelligence.
   Application software.
   Data mining.
   Optical data processing.
   Biometrics (Biology).
   Algorithms.
   Artificial Intelligence.
   Information Systems Applications (incl. Internet).
   Data Mining and Knowledge Discovery.
   Image Processing and Computer Vision.
   Biometrics.
   Algorithm Analysis and Problem Complexity.
Анотація: This book constitutes the refereed post-conference proceedings of the 5th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2017, held in Hyderabad, India, in December 2017. The 40 full papers presented were carefully reviewed and selected from 139 submissions. The papers were grouped into various subtopics including arti ficial intelligence, machine learning, image processing, pattern recognition, speech processing, information retrieval, natural language processing, social network analysis, security, and fuzzy rough sets.

Перейти: https://doi.org/10.1007/978-3-319-71928-3

Дод.точки доступу:
Ghosh, Ashish. \ed.\; Pal, Rajarshi. \ed.\; Prasath, Rajendra. \ed.\; SpringerLink (Online service)
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18.

    Proceedings of International Conference on Computer Vision and Image Processing [[electronic resource] :] : CVIP 2016, Volume 2 / / ed. Raman, Balasubramanian. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XIV, 567 p. 267 illus. - Б. ц.
    Зміст:
Chapter 1. Fingerprint Image Segmentation using Textural Features --
Chapter 2. Improved Feature Selection for Neighbor Embedding Super-Resolution using Zernike Moments --
Chapter 3. Target Recognition in Infrared Imagery using Convolutional Neural Network --
Chapter 4. Selected Context Dependent Prediction for Reversible Watermarking with Optimal Embedding --
Chapter 5. Cancelable Biometrics using Hadamard Transform and Friendly Random Projections --
Chapter 6. A Semi-Automated Method for Object Segmentation in Infant’s Egocentric Video to Study Object Perception --
Chapter 7. A Novel Visual Secret Sharing Scheme using Affine Cipher and Image Interleaving --
Chapter 8. Comprehensive Representation and Efficient Extraction of Spatial Information for Human Activity Recognition from Video Data --
Chapter 9. Robust Pose Recognition using Deep Learning --
Chapter 10. A Robust Scheme for Extraction of Text Lines from Handwritten Documents --
Chapter 11. Palmprint Recognition Based on Minutiae Quadruplets --
Chapter 12. Human Action Recognition for Depth Cameras via Dynamic Frame Warping --
Chapter 13. Reference Based Image Encoding --
Chapter 14. Improving Face Detection in Blurred Videos for Surveillance Applications --
Chapter 15. Support Vector Machine Based Extraction of Crime Information in Human Brain using ERP Image --
Chapter 16. View Invariant Motorcycle Detection for Helmet Wear Analysis in Intelligent Traffic Surveillance --
Chapter 17. Morphological Geodesic Active Contour Based Automatic Aorta Segmentation in Thoracic CT Images --
Chapter 18. Surveillance Video Synopsis while Preserving Object Motion Structure and Interaction --
Chapter 19. Face Expression Recognition using Histograms of Oriented Gradients with Reduced Features --
Chapter 20. Dicentric Chromosome Image Classification using Fourier Domain Based Shape Descriptors and Support Vector Machine --
Chapter 21. An Automated Ear Localization Technique Based on Modified Hausdorff Distance --
Chapter 22. Sclera Pattern Synthesis Based on Non-parametric Texture Synthesis Technique --
Chapter 23. Virtual 3-D Walkthrough for Intelligent Emergency Response --
Chapter 24. Spontaneous vs. Posed smiles – Can We Tell the Difference? --
Chapter 25. Handling Illumination Variation: A Challenge for Face Recognition --
Chapter 26. Bin Picking Using Manifold Learning --
Chapter 27. Motion Estimation From Image Sequences: A Fractional Order Total Variation Model --
Chapter 28. Script Identification in Natural Scene Images: A Dataset and Texture-Feature Based Performance Evaluation --
Chapter 29. Posture Recognition in HINE Exercises --
Chapter 30. Multi-Oriented Text Detection from Video using Sub-Pixel Mapping --
Chapter 31. Efficient Framework for Action Recognition using Reduced Fisher Vector Encoding --
Chapter 32. Detection Algorithm for Copy-Move Forgery Based on Circle Block --
Chapter 33. FPGA Implementation of GMM Algorithm for Background Subtractions in Video Sequences --
Chapter 34. Site Suitability Evaluation for Urban Development using Remote Sensing, GIS & Analytic Hierarchy Process (AHP) --
Chapter 35. A Hierarchical Shot Boundary Detection Algorithm using Global and Local Features --
Chapter 36. Analysis of Comparators for Binary Watermarks --
Chapter 37. On Sphering the High Resolution Satellite Image using Fixed Point Based ICA Approach --
Chapter 38. A Novel Fuzzy Based Satellite Image Enhancement --
Chapter 39. Differentiating Photographic and PRCG Images: Using Tampering Localization Features --
Chapter 40. A Novel Chaos Based Robust Watermarking Framework --
Chapter 41. Deep Gesture: Static Hand Gesture Recognition using CNN --
Chapter 42. A Redened Codebook Model for Dynamic Backgrounds --
Chapter 43. Reassigned Time Frequency Distribution Based Face Recognition --
Chapter 44. Image Registration of Medical Images using Ripplet Transform --
Chapter 45. 3D Local Transform Patterns: A New Feature Descriptor for Image Retrieval --
Chapter 46. Quaternion Circularly Semi-Orthogonal Moments for Invariant Image Recognition --
Chapter 47. Study of Zone-Based Feature for Online Handwritten Signature Recognition and Verification in Devanagari Script --
Chapter 48. Leaf Identification using Shape and Texture Features --
Chapter 49. Depth Image Super-Resolution: A Review and Wavelet Perspective --
Chapter 50. On-line Gesture Based User Authentication System Robust to Shoulder Surfing.
Рубрики: Signal processing.
   Image processing.
   Speech processing systems.
   Optical data processing.
   Biometrics (Biology).
   Signal, Image and Speech Processing.
   Image Processing and Computer Vision.
   Biometrics.
Анотація: This edited volume contains technical contributions in the field of computer vision and image processing presented at the First International Conference on Computer Vision and Image Processing (CVIP 2016). The contributions are thematically divided based on their relation to operations at the lower, middle and higher levels of vision systems, and their applications. The technical contributions in the areas of sensors, acquisition, visualization and enhancement are classified as related to low-level operations. They discuss various modern topics – reconfigurable image system architecture, Scheimpflug camera calibration, real-time autofocusing, climate visualization, tone mapping, super-resolution and image resizing. The technical contributions in the areas of segmentation and retrieval are classified as related to mid-level operations. They discuss some state-of-the-art techniques – non-rigid image registration, iterative image partitioning, egocentric object detection and video shot boundary detection. The technical contributions in the areas of classification and retrieval are categorized as related to high-level operations. They discuss some state-of-the-art approaches – extreme learning machines, and target, gesture and action recognition. A non-regularized state preserving extreme learning machine is presented for natural scene classification. An algorithm for human action recognition through dynamic frame warping based on depth cues is given. Target recognition in night vision through convolutional neural network is also presented. Use of convolutional neural network in detecting static hand gesture is also discussed. Finally, the technical contributions in the areas of surveillance, coding and data security, and biometrics and document processing are considered as applications of computer vision and image processing. They discuss some contemporary applications. A few of them are a system for tackling blind curves, a quick reaction target acquisition and tracking system, an algorithm to detect for copy-move forgery based on circle block, a novel visual secret sharing scheme using affine cipher and image interleaving, a finger knuckle print recognition system based on wavelet and Gabor filtering, and a palmprint recognition based on minutiae quadruplets.

Перейти: https://doi.org/10.1007/978-981-10-2107-7

Дод.точки доступу:
Raman, Balasubramanian. \ed.\; Kumar, Sanjeev. \ed.\; Roy, Partha Pratim. \ed.\; Sen, Debashis. \ed.\; SpringerLink (Online service)
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19.

    de Winter, Winter, Joost C. F.
    Human Subject Research for Engineers [[electronic resource] :] : a Practical Guide / / Winter, Joost C. F. de Winter, Dodou, Dimitra. ; . - 1st ed. 2017. - [S. l. : s. n.]. - IX, 105 p. 23 illus., 9 illus. in color. - Б. ц.
    Зміст:
1 Scientific Method, Human Research Ethics, and Biosafety/Biosecurity --
2 Experimental Design --
3 Statistics --
4 Publishing --
MATLAB Scripts.
Рубрики: Engineering design.
   Control engineering.
   Robotics.
   Mechatronics.
   Engineering ethics.
   User interfaces (Computer systems).
   Statistics .
   Biometrics (Biology).
   Engineering Design.
   Control, Robotics, Mechatronics.
   Engineering Ethics.
   User Interfaces and Human Computer Interaction.
   Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
   Biometrics.
Анотація: This Brief introduces engineers to the main principles in ethics, research design, statistics, and publishing of human subject research. In recent years, engineering has become strongly connected to disciplines such as biology, medicine, and psychology. Often, engineers (and engineering students) are expected to perform human subject research. Typical human subject research topics conducted by engineers include human-computer interaction (e.g., evaluating the usability of software), exoskeletons, virtual reality, teleoperation, modelling of human behaviour and decision making (often within the framework of ‘big data’ research), product evaluation, biometrics, behavioural tracking (e.g., of work and travel patterns, or mobile phone use), transport and planning (e.g., an analysis of flows or safety issues), etc. Thus, it can be said that knowledge on how to do human subject research is indispensable for a substantial portion of engineers. Engineers are generally well trained in calculus and mechanics, but may lack the appropriate knowledge on how to do research with human participants. In order to do high-quality human subject research in an ethical manner, several guidelines have to be followed and pitfalls have to be avoided. This book discusses these guidelines and pitfalls. The aim is to prepare engineers and engineering students to carry out independent research in a responsible manner.

Перейти: https://doi.org/10.1007/978-3-319-56964-2

Дод.точки доступу:
Dodou, Dimitra.; de Winter, Joost C.F. \.\; SpringerLink (Online service)
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20.

    Biometric Security and Privacy [[electronic resource] :] : opportunities & Challenges in The Big Data Era / / ed. Jiang, Richard. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - VIII, 424 p. 194 illus., 146 illus. in color. - Б. ц.
    Зміст:
Introduction --
Part I – New Methods in Biometrics --
Deep Biometrics: A Robust Approach to Biometrics in Big Data Issues --
Multimodal Biometric Fusion via Ensemble Learning --
Fuzzy Logic for Precise Biometric Systems --
Hierarchical Biometric Verification with Sparse Features --
Dynamic Programming for Biometric Verification --
Part II – New Advances in Various Biometric Technologies --
Paleographic Handwriting Analysis for Author Identification --
Palmprints versus Fingerprints: Rivals or Friends? --
A Survey on Soft Biometrics for Forensic Analysis --
Robust Biometric Verification with Low Quality Data --
Streamed Biometric Verification for Big Data Challenge --
Privacy-Protected Biometric Verification for Security Applications --
Part III – New Applications using Biometrics --
Biometric Key Generation using Fuzzy Extractor for Mobile Banking --
Securing Electronic Medical Records Using Biometric Authentication --
Body Biometrics from MRI Images for Medicine Advice --
Identify Invisible Persons in Social Network --
From Memory to Human Recognition in Cognitive Robots.
Рубрики: Signal processing.
   Image processing.
   Speech processing systems.
   Biometrics (Biology).
   Big data.
   User interfaces (Computer systems).
   System safety.
   Computer security.
   Signal, Image and Speech Processing.
   Biometrics.
   Big Data/Analytics.
   User Interfaces and Human Computer Interaction.
   Security Science and Technology.
   Systems and Data Security.
Анотація: This book highlights recent research advances on biometrics using new methods such as deep learning, nonlinear graph embedding, fuzzy approaches, and ensemble learning. Included are special biometric technologies related to privacy and security issues, such as quality issue, biometric template protection, and anti-spoofing. The book also focuses on several emerging topics such as big data issues, mobile biometrics and multispectral biometrics, and includes a number of new biometrics such as vein pattern, acoustic biometrics, eye-blinking EOG, ECG, gait and handwriting. Authors also show how to use biometrics in cyber security applications and its relevant legal matters under EU legislation. The contributors cover the topics, their methods, and their applications in depth.

Перейти: https://doi.org/10.1007/978-3-319-47301-7

Дод.точки доступу:
Jiang, Richard. \ed.\; Al-maadeed, Somaya. \ed.\; Bouridane, Ahmed. \ed.\; Crookes, Prof. Danny. \ed.\; Beghdadi, Azeddine. \ed.\; SpringerLink (Online service)
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(Асоціація ЕБНІТ)