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biometrics IRB Best Practices and Data Protection 12

Biometrics IRB Best Practices and Data Protection

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conference on biometric and surveillance technology for human and activity identification xii

作者： Boehnen, Christopher Bolme, David Flynn, Patrick Oak Ridge Natl Lab Oak Ridge TN 37831 USA Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9781628415735

The collection of data from human subjects for biometrics research in the United States requires the development of a data collection protocol that is reviewed by a human Subjects Institutional Review Board (IRB). The IRB reviews the protocol for risks and approves it if it meets the criteria for approval specified in the relevant Federal regulations (45 CFR 46). Many other countries operate similar mechanisms for the protection of human subjects. IRBs review protocols for safety, confidentiality, and for minimization of risk associated with identity disclosure. Since biometric measurements are potentially identifying, IRB scrutiny of biometrics data collection protocols can be expected to be thorough. This paper discusses the intricacies of IRB best practices within the worldwide biometrics community. This is important because research decisions involving human subjects are made at a local level and do not set a precedent for decisions made by another IRB board. In many cases, what one board approves is not approved by another board, resulting in significant inconsistencies that prove detrimental to both researchers and human subjects. Furthermore, the level of biometrics expertise may be low on IRBs, which can contribute to the unevenness of reviews. This publication will suggest possible best practices for designing and seeking IRB approval for human subjects research involving biometrics measurements. The views expressed are the opinions of the authors.

关键词： biometrics IRB Institutional Review Board human Subjects Research Data

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human Task Performance Baseline: Results from a Cross-Band Facial identification Perception Study 12

Human Task Performance Baseline: Results from a Cross-Band F...

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conference on biometric and surveillance technology for human and activity identification xii

作者： Byrd, Kenneth A. Choi, Hee-Sue US Army RDECOM CERDEC Night Vis & Elect Sensor Di Washington DC USA

ISBN: (纸本)9781628415735

Cross-band facial recognition is a difficult task, even for the most robust matching algorithms. Inherent factors such as camera effects (blur, noise, and sampling), and variation in pose and illumination, are known to negatively affect algorithm performance. Because cross-band matching algorithms are in the infancy of development, it is currently unclear if their performance is superior to human observers performing this task. In this paper, we present findings from a pilot study aimed at analyzing the ability of an ensemble of human observers to perform the 1:N cross-band facial identification task on degraded facial images, where the probe and gallery images were captured in different spectral bands (visible, SWIR, MWIR and LWIR). Results from our 11-alternative forced choice perception study indicate that: 1) a group of observers familiar with even a subset of subjects in a gallery set are, on average, able to perform the task with higher probability (p > 0.15) than a group of observers with no prior exposure, and 2) task performance for both the familiar and unfamiliar groups increased 1.5-3.4% when matching multi-spectral probe images to galleries of 24-bit color facial images vs. 8-bit monochrome facial images. For the SWIR case, however, we observed a 9.1% increase in performance with 24-bit facial images vs. 8-bit facial images. Results from this study can be leveraged for future work directly comparing cross-band matching performance of humans vs. algorithms.

关键词： Facial identification Facial Recognition human Perception biometrics

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Privacy Information Management for Video surveillance

Privacy Information Management for Video Surveillance

引用

conference on biometric and surveillance technology for human and activity identification X

作者： Luo, Ying Cheung, Sen-ching S. Univ Kentucky Elect & Comp Engn Dept Lexington KY 40507 USA

ISBN: (纸本)9780819495037

The widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been proposed to automatically redact images of trusted individuals in the surveillance video. To identify these individuals for protection, the most reliable approach is to use biometric signals such as iris patterns as they are immutable and highly discriminative. In this paper, we propose a privacy data management system to be used in a privacy-aware video surveillance system. The privacy status of a subject is anonymously determined based on her iris pattern. For a trusted subject, the surveillance video is redacted and the original imagery is considered to be the privacy information. Our proposed system allows a subject to access her privacy information via the same biometric signal for privacy status determination. Two secure protocols, one for privacy information encryption and the other for privacy information retrieval are proposed. Error control coding is used to cope with the variability in iris patterns and efficient implementation is achieved using surrogate data records. Experimental results on a public iris biometric database demonstrate the validity of our framework.

关键词： Private Information Management Video surveillance Iris matching Error Control Coding

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human Thermal Modeling to Augment MWIR Image Analysis in surveillance Applications

Human Thermal Modeling to Augment MWIR Image Analysis in Sur...

引用

conference on biometric and surveillance technology for human and activity identification XI

作者： Woodyard, R. L. Skipper, J. A. Wright State Univ Dept Biomed Ind & Human Factors Engn Dayton OH 45435 USA Wright State Univ Wright State Res Inst Dayton OH 45435 USA

ISBN: (纸本)9781628410129

The interpretation of thermal imagery can be augmented with information derived from human thermal modeling to better infer human activity during, or prior to, data capture. This additional insight into human activity could prove useful in security and surveillance applications. We have implemented Tanabe's 65 NM thermocomfort model to predict skin surface temperature under a wide variety of environmental, activity and body parameters. Here, humans are modeled as sixteen segments (head, chest, upper leg, etc.), wherein spherical geometry is assumed for the head and cylindrical geometry is assumed for all other segments. Each segment is comprised of four layers: core, muscle, fat, and skin. Clothing is modeled as an additional layer (or layers) of resistance. Users supply input parameters via our custom MATLAB graphical user interface that includes a robust clothing database based on McCullough's A Database for Determining the Evaporative Resistance of Clothing, and then Tanabe's bioheat equations are solved to predict skin temperatures of each body segment. As an initial step of model validation, we compared our computed thermal resistances with literature values. Our evaporative and dry resistance on a per segment basis agreed with literature values. The dry resistance of each segment varied no more than .03 [m(2)degrees C/W]. Model validation will be extended to compare the results of our human subject trials (known body parameters, clothing, environmental factors and activity levels) to model outputs. Agreement would further substantiate the propagation of model-predicted skin temperatures through the thermal imager's transfer function to predict human heat signatures in thermal imagery.

关键词： human thermal modeling thermal manikins thermal imaging MWIR imaging surveillance

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An Overview of Suite for Automated Global Electronic Biosurveillance (SAGES)

An Overview of Suite for Automated Global Electronic Biosurv...

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conference on Sensing Technologies for Global Health, Military Medicine, Disaster Response and Environmental Monitoring II - and biometric technology for human identification IX

作者： Lewis, Sheri L. Feighner, Brian H. Loschen, Wayne A. Wojcik, Richard A. Skora, Joseph F. Coberly, Jacqueline S. Blazes, David L. Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Armed Force Hlth Surveillance Ctr Div GEIS Operat Silver Spring MD 20904 USA

ISBN: (纸本)9780819490490

Public health surveillance is undergoing a revolution driven by advances in the field of information technology. Many countries have experienced vast improvements in the collection, ingestion, analysis, visualization, and dissemination of public health data. Resource-limited countries have lagged behind due to challenges in information technology infrastructure, public health resources, and the costs of proprietary software. The Suite for Automated Global Electronic biosurveillance (SAGES) is a collection of modular, flexible, freely-available software tools for electronic disease surveillance in resource-limited settings. One or more SAGES tools may be used in concert with existing surveillance applications or the SAGES tools may be used en masse for an end-to-end biosurveillance capability. This flexibility allows for the development of an inexpensive, customized, and sustainable disease surveillance system. The ability to rapidly assess anomalous disease activity may lead to more efficient use of limited resources and better compliance with World Health Organization International Health Regulations.

关键词： surveillance public health biosurveillance SAGES resource-limited

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Multi-Feature Embedding and Deep Classification for Elderly activity Recognition

Multi-Feature Embedding and Deep Classification for Elderly ...

引用

2023 International conference on Data Science and Network Security, ICDSNS 2023

作者： Reddy, B. Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350301595

human activity Recognition (HAR) has many potential applications in the real world, including biometric user identification, elderly health monitoring, and governmental surveillance, and as a result, it is currently the subject of intensive study. HAR has emerged as a major focus in the fields of mobile and omnipresent computing because to the increasing prevalence of wearable sensors and the Internet of Things. In recent years, deep learning has been the dominant method for making inferences and solving problems in the HAR system. However, significant difficulties remain in applying HAR to issues in biometric user identification, whereby a wide range of human behaviors may be considered biometric features and utilized to distinguish individuals. Using deep learning models to recognize human behavior as its foundation, this study presents an original framework for multi-class wearable identification of users. Sensory data from the wearable devices' tri-axial gyroscopic and tri-axial accelerometers are used to learn more about users while they engage in a variety of activities. Further, the usefulness of the suggested framework was shown by a series of tests. According to the results, the Convolutional Neural Network's model had the most overall accuracy (91.77%) across all users, while the Long Short-Term Memory machine learning model achieved the highest overall accuracy (92.43%). Regarding biometric user identification, both of them are good enough. © 2023 IEEE.

关键词： biometrics

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Mobile User Identity Sensing Using the Motion Sensor

Mobile User Identity Sensing Using the Motion Sensor

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conference on biometric and surveillance technology for human and activity identification XI

作者： Zhao, Xi Feng, Tao Xu, Lei Shi, Weidong Univ Houston Dept Comp Sci Houston TX 77204 USA

ISBN: (纸本)9781628410129

Employing mobile sensor data to recognize user behavioral activities has been well studied in recent years. However, to adopt the data as a biometric modality has rarely been explored. Existing methods either used the data to recognize gait, which is considered as a distinguished identity feature;or segmented a specific kind of motion for user recognition, such as phone picking-up motion. Since the identity and the motion gesture jointly affect motion data, to fix the gesture (walking or phone picking-up) definitively simplifies the identity sensing problem. However, it meanwhile introduces the complexity from gesture detection or requirement on a higher sample rate from motion sensor readings, which may draw the battery fast and affect the usability of the phone. In general, it is still under investigation that motion based user authentication in a large scale satisfies the accuracy requirement as a stand-alone biometrics modality. In this paper, we propose a novel approach to use the motion sensor readings for user identity sensing. Instead of decoupling the user identity from a gesture, we reasonably assume users have their own distinguishing phone usage habits and extract the identity from fuzzy activity patterns, represented by a combination of body movements whose signals in chains span in relative low frequency spectrum and hand movements whose signals span in relative high frequency spectrum. Then Bayesian Rules are applied to analyze the dependency of different frequency components in the signals. During testing, a posterior probability of user identity given the observed chains can be computed for authentication. Tested on an accelerometer dataset with 347 users, our approach has demonstrated the promising results.

关键词： identity sensing motion fuzzy activity patterns

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human activity Recognition by BiLSTM Recurrent Neural Networks and Support Vector Machine 6

Human Activity Recognition by BiLSTM Recurrent Neural Networ...

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6th Iraqi International conference on Engineering technology and its Applications, IICETA 2023

作者： Ridha, Ameer Ali Almaameri, Ihab Blazovics, Laszlo Abbas, Haider Mohammed Education in Najaf Ministry of Education Najaf Iraq Budapest University of Technology and Economics Department of Automation and Applied Informatics Budapest Hungary The Islamic University Najaf Iraq

ISBN: (纸本)9798350303339

In the last years of this decade, the recognition of human activity has become important to a wide range of researchers in pattern recognition and human-computer interaction as a result of its wide range of real-world applications, such as gesture recognition, biometric user identification, surveillance by authorities, behavior analysis and health monitoring of the elderly. human activity Recognition (HAR) has become a significant topic in mobile and ubiquitous computing as a result of the widespread use of wearable sensor devices and the Internet of Things (loT). Deep Learning (DL) is one of the most commonly used problem-solving techniques in the HAR system. Nevertheless, there are major challenges in applying HAR to problems in recognizing various human activities. In this paper, presented and showed the activities of implementing a new combination of DL methods for multi-class user activity identification to HAR. Using DL methods can be extracting discriminative features automatically from raw sensor data. Specifically, in this work, we proposed a hybrid architecture that features a combination of Bidirectional Long Short-Term Memory (BILSTM) networks and support vector Machines (SVM) for the HAR task. The UCI HAR dataset is used to test the model, it consists of accelerometer and gyroscope data obtained from smartphones. The dataset is split into 30 % for testing and 70% for training. The results for the (BILSTM-SVM) model, showed that the highest accuracy for all users was 98.74 %, higher than all previous models using the same dataset. © 2023 IEEE.

关键词： Support vector machines

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Multi-Feature Embedding and Deep Classification for Elderly activity Recognition

Multi-Feature Embedding and Deep Classification for Elderly ...

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Data Science and Network Security (ICDSNS), International conference on

作者： B Reddy Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Punjab India

human activity Recognition (HAR) has many potential applications in the real world, including biometric user identification, elderly health monitoring, and governmental surveillance, and as a result, it is currently the subject of intensive study. HAR has emerged as a major focus in the fields of mobile and omnipresent computing because to the increasing prevalence of wearable sensors and the Internet of Things. In recent years, deep learning has been the dominant method for making inferences and solving problems in the HAR system. However, significant difficulties remain in applying HAR to issues in biometric user identification, whereby a wide range of human behaviors may be considered biometric features and utilized to distinguish individuals. Using deep learning models to recognize human behavior as its foundation, this study presents an original framework for multi-class wearable identification of users. Sensory data from the wearable devices' tri-axial gyroscopic and tri-axial accelerometers are used to learn more about users while they engage in a variety of activities. Further, the usefulness of the suggested framework was shown by a series of tests. According to the results, the Convolutional Neural Network's model had the most overall accuracy (91.77 % ) across all users, while the Long Short-Term Memory machine learning model achieved the highest overall accuracy (92.43%). Regarding biometric user identification, both of them are good enough.

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human activity Recognition by BiLSTM Recurrent Neural Networks and Support Vector Machine

Human Activity Recognition by BiLSTM Recurrent Neural Networ...

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International conference on Engineering technology and their Applications (IICETA)

作者： Ameer Ali Ridha Ihab Almaameri László Blázovics Haider Mohammed Abbas Ministry of Education General Directorate of Education in Najaf Najaf Iraq Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary The Islamic University Najaf Iraq

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