Machine Learning Techniques for Gait Biometric Recognition

Machine Learning Techniques for Gait Biometric Recognition

This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric.

Author: James Eric Mason

Publisher: Springer

ISBN: 9783319290881

Category: Technology & Engineering

Page: 223

View: 180

This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition · provides detailed discussions of key research challenges and open research issues in gait biometrics recognition · compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear
Categories: Technology & Engineering

Examining the Impact of Normalization and Footwear on Gait Biometrics Recognition Using the Ground Reaction Force

Examining the Impact of Normalization and Footwear on Gait Biometrics Recognition Using the Ground Reaction Force

This is a biometric for which the computational power required for practical applications in a security setting has only recently become available.

Author: James Eric Mason

Publisher:

ISBN: OCLC:897619894

Category:

Page:

View: 726

Behavioural biometrics are unique non-physical human characteristics that can be used to distinguish one person from another. One such characteristic, which belongs to the Gait Biometric, is the footstep Ground Reaction Force (GRF), the temporal signature of the force exerted by the ground back on the foot through the course of a footstep. This is a biometric for which the computational power required for practical applications in a security setting has only recently become available. In spite of this, there are still barriers to deployment in a practical setting, including large research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition. In this thesis we devised an experiment to address these research gaps, while also expanding upon the biometric system research presented in previous GRF recognition studies. To assess the effect of footwear on recognition performance we proposed the analysis of a dataset containing samples for two different types of running shoes. While, with regards to stepping speed, we set out to demonstrate that normalizing for step duration will mitigate speed variation biases and improve GRF recognition performance; this included the development of two novel machine learning-based temporal normalization techniques: Localized Least Squares Regression (LLSR) and Localized Least Squares Regression with Dynamic Time Warping (LLSRDTW). Moreover, building upon previous research, biometric system analysis was done over four feature extractors, seven normalizers, and five different classifiers, allowing us to indirectly compare the GRF recognition results for biometric system configurations that had never before been directly compared. The results achieved for the aforementioned experiment were generally in line with our initial assumptions. Comparing biometrics systems trained and tested with the same footwear against those trained and tested with different footwear, we found an average decrease in recognition performance of about 50% ... .
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Computational Intelligence in Sensor Networks

Computational Intelligence in Sensor Networks

... Munih, M.: Development and validation of a wearable inertial measurement system for use with lower limb exoskeletons. ... I., Woungang, I.: Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force.

Author: Bijan Bihari Mishra

Publisher: Springer

ISBN: 9783662572771

Category: Technology & Engineering

Page: 488

View: 350

This book discusses applications of computational intelligence in sensor networks. Consisting of twenty chapters, it addresses topics ranging from small-scale data processing to big data processing realized through sensor nodes with the help of computational approaches. Advances in sensor technology and computer networks have enabled sensor networks to evolve from small systems of large sensors to large nets of miniature sensors, from wired communications to wireless communications, and from static to dynamic network topology. In spite of these technological advances, sensor networks still face the challenges of communicating and processing large amounts of imprecise and partial data in resource-constrained environments. Further, optimal deployment of sensors in an environment is also seen as an intractable problem. On the other hand, computational intelligence techniques like neural networks, evolutionary computation, swarm intelligence, and fuzzy systems are gaining popularity in solving intractable problems in various disciplines including sensor networks. The contributions combine the best attributes of these two distinct fields, offering readers a comprehensive overview of the emerging research areas and presenting first-hand experience of a variety of computational intelligence approaches in sensor networks.
Categories: Technology & Engineering

Computer Information Systems and Industrial Management

Computer Information Systems and Industrial Management

Sensors 15, 932–964 (2015) Mason, James Eric, Traoré, Issa, Woungang, Isaac: Machine Learning Techniques for Gait Biometric Recognition. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29088-1 Matovski, D.S., Nixon, M.S., ...

Author: Khalid Saeed

Publisher: Springer Nature

ISBN: 9783030476793

Category: Computers

Page: 486

View: 433

This book constitutes the proceedings of the 19th International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2020, held in Bialystok, Poland, in October 2020. Due to the COVID-19 pandemic the conference has been postponed to October 2020. The 40 full papers presented together with 5 abstracts of keynotes were carefully reviewed and selected from 62 submissions. The main topics covered by the chapters in this book are biometrics, security systems, multimedia, classification and clustering, industrial management. Besides these, the reader will find interesting papers on computer information systems as applied to wireless networks, computer graphics, and intelligent systems. The papers are organized in the following topical sections: biometrics and pattern recognition applications; computer information systems and security; industrial management and other applications; machine learning and high performance computing; modelling and optimization.
Categories: Computers

Sensors for Gait Posture and Health Monitoring Volume 2

Sensors for Gait  Posture  and Health Monitoring Volume 2

Bouchrika, I.; Goffredo, M.; Carter, J.; Nixon, M. On using gait in forensic biometrics. ... Moustakidis, S.P.; Theocharis, J.B.; Giakas, G. Subject recognition based on ground reaction force measurements of gait signals. IEEE Trans.

Author: Thurmon Lockhart

Publisher: MDPI

ISBN: 9783039363445

Category: Medical

Page: 392

View: 646

In recent years, many technologies for gait and posture assessments have emerged. Wearable sensors, active and passive in-house monitors, and many combinations thereof all promise to provide accurate measures of physical activity, gait, and posture parameters. Motivated by market projections for wearable technologies and driven by recent technological innovations in wearable sensors (MEMs, electronic textiles, wireless communications, etc.), wearable health/performance research is growing rapidly and has the potential to transform future healthcare from disease treatment to disease prevention. The objective of this Special Issue is to address and disseminate the latest gait, posture, and activity monitoring systems as well as various mathematical models/methods that characterize mobility functions. This Special Issue focuses on wearable monitoring systems and physical sensors, and its mathematical models can be utilized in varied environments under varied conditions to monitor health and performance
Categories: Medical

IT Policy and Ethics Concepts Methodologies Tools and Applications

IT Policy and Ethics  Concepts  Methodologies  Tools  and Applications

To improve the performance of the recognition system, some form of machine learning may be used to simulate human ... capturing gait-related information which is impossible to capture using video sensors such as ground reaction force, ...

Author: Management Association, Information Resources

Publisher: IGI Global

ISBN: 9781466629202

Category: Technology & Engineering

Page: 2036

View: 993

IT policies are set in place to streamline the preparation and development of information communication technologies in a particular setting. IT Policy and Ethics: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the features of modern organizations in order to advance the understanding of IT standards. This is an essential reference source for researchers, scholars, policymakers, and IT managers as well as organizations interested in carrying out research in IT policies.
Categories: Technology & Engineering

Dynamics of Coupled Structures Volume 4

Dynamics of Coupled Structures  Volume 4

Middleton, L., Buss, A.A., Bazin, A., Nixon, M.S.: A floor sensor system for gait recognition. ... 1447–1460 (2010) Kohle, M., Merkl, D.: Identification of gait patterns with self-organizing maps based on ground reaction force.

Author: Matt Allen

Publisher: Springer

ISBN: 9783319297637

Category: Technology & Engineering

Page: 528

View: 386

Dynamics of Coupled Structures, Volume 4. Proceedings of the 34th IMAC, A Conference and Exposition on Dynamics of Multiphysical Systems: From Active Materials to Vibroacoustics, 2016, the fourth volume of ten from the Conference brings together contributions to this important area of research and engineering. Th e collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: • Experimental Dynamic Substructuring • Structural Coupling of Nonlinear Structures • Analytical/Numerical Modeling of Joints • Industrial Applications of Substructuring • Source Identifi cation & Transfer Path Analysis • Human Induced Vibrations • Damping & Friction
Categories: Technology & Engineering

Applied Technologies

Applied Technologies

Such synthetic gait patterns can be used for example, in data augmentation strategies. ... the multivariate series produced as output by our method is an approximation of the ground truth curves with superior quality and personalization ...

Author: Miguel Botto-Tobar

Publisher: Springer Nature

ISBN: 9783030425203

Category: Computers

Page: 669

View: 568

This second volume of the three-volume set (CCIS 1193, CCIS 1194, and CCIS 1195) constitutes the refereed proceedings of the First International Conference on Applied Technologies, ICAT 2019, held in Quito, Ecuador, in December 2019. The 124 full papers were carefully reviewed and selected from 328 submissions. The papers are organized according to the following topics: technology trends; computing; intelligent systems; machine vision; security; communication; electronics; e-learning; e-government; e-participation.
Categories: Computers

Wearable Systems Based Gait Monitoring and Analysis

Wearable Systems Based Gait Monitoring and Analysis

C. Fang, Y. Wang, S. Gao, Flexible and wearable GRF and EMG sensors enabled locomotion mode recognition for IoHT based ... M. Lee, J. Ryu, I. Youn, Biometric personal identification based on gait analysis using surface EMG signals, ...

Author: Shuo Gao

Publisher: Springer Nature

ISBN: 9783030973322

Category:

Page:

View: 310

Categories:

Biometrics under Biomedical Considerations

Biometrics under Biomedical Considerations

Based on the above analysis, better gait analysis would improve learning algorithms that maximizes the performance of the biometric legged locomotion machine! References 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

Author: Amine Nait-Ali

Publisher: Springer

ISBN: 9789811311444

Category: Technology & Engineering

Page: 280

View: 960

This book addresses biometrics from a biomedical engineering point of view. Divided into five sections, it discusses topics including the influence of pathologies on various biometric modalities (e.g. face, iris, fingerprint), medical and security biometrics, behavioural biometrics, instrumentation, wearable technologies and imaging. The final chapters also present a number of case studies. The book is suitable for advanced graduate and postgraduate students, engineers and researchers, especially those in signal and image processing, biometrics, and biomedical engineering.
Categories: Technology & Engineering