The Experts below are selected from a list of 57627 Experts worldwide ranked by ideXlab platform
Monika Zalnieriute - One of the best experts on this subject based on the ideXlab platform.
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gender justice and freedom of expression from public spaces to digital platforms and Facial Recognition technologies
Social Science Research Network, 2021Co-Authors: Monika ZalnieriuteAbstract:In this submission, I discuss how women in the public sphere, including women journalists, human rights defenders, politicians, and activists face many challenges in exercising their freedom of expression and opinion across the globe. Both online and in public spaces in our cities, which are increasingly surveilled and monitored by government and law enforcement agencies, women face challenges. In this submission, I invite the UN Special Rapporteur to: 1) Call on the UN bodies to enhance their understanding of theory intersectionality. I have recently proposed a way to enhance judicial interpretation of reconceptualising by reference to a modified concept of “harmful cultural practices”. 2) Call for a ban (or at least a suspension) on the use of Facial Recognition technology by governments pending independent scrutiny of the discriminatory impacts the technology may have against women and other protected groups. 3) Call for the development of binding international human rights law for private actors to remedy the violations of freedom of expression of women and LGBTI communities in the digital environment.
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burning bridges the automated Facial Recognition technology and public space surveillance in the modern state
Social Science Research Network, 2021Co-Authors: Monika ZalnieriuteAbstract:A live automated Facial Recognition technology, rolled out in public spaces and cities across the world, is transforming the nature of modern policing. In R (on the application of Bridges) v Chief Constable of South Wales Police, decided in August 2020 (‘Bridges’) – the first successful legal challenge to automated Facial Recognition technology worldwide - the Court of Appeal in the United Kingdom held that the use of automated Facial Recognition technology by the South Wales Police was unlawful. This landmark ruling can set a precedent and influence future policy on Facial Recognition in many countries. Bridges decision imposes some limits on the previously unconstrained police discretion on whom to target and where to deploy the technology. Yet, while the decision demands a clearer legal framework to limit the discretion of police who use such technology, it does not, in principle, oppose the use of Facial Recognition technology for mass-surveillance in public places, nor for monitoring political protests. To the contrary, the Court accepted that the use of automated Facial Recognition in public spaces to identify very large numbers of people and to track their movements is proportional to law enforcement goals. Thus, the Court dismissed the wider impact and significant risks posed by using Facial Recognition technology in public spaces; it underplayed the heavy burden placed on democratic participation and the rights to freedom of expression and association, which require collective action in public spaces. Neither did the Court demand transparency about the technologies used by the police force, which is often shielded behind the ‘trade secrets’ by the corporations who produce them, nor did the Court act to prevent fragmentation and inconsistency between local police forces’ rules and regulations on automated Facial Recognition technology, which leaves the law less predictable. Thus, while the Bridges decision is reassuring and demands change in the discretionary approaches of UK police in the short term, its long-term impact in burning bridges between the expanding public space surveillance infrastructure and the modern state is less certain.
M Snyder - One of the best experts on this subject based on the ideXlab platform.
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implementation of Facial Recognition with microsoft kinect v2 sensor for patient verification
Medical Physics, 2017Co-Authors: Evan Silverstein, M SnyderAbstract:Purpose The aim of this study was to present a straightforward implementation of Facial Recognition using the Microsoft Kinect v2 sensor for patient identification in a radiotherapy setting. Materials and methods A Facial Recognition system was created with the Microsoft Kinect v2 using a Facial mapping library distributed with the Kinect v2 SDK as a basis for the algorithm. The system extracts 31 fiducial points representing various Facial landmarks which are used in both the creation of a reference data set and subsequent evaluations of real-time sensor data in the matching algorithm. To test the algorithm, a database of 39 faces was created, each with 465 vectors derived from the fiducial points, and a one-to-one matching procedure was performed to obtain sensitivity and specificity data of the Facial identification system. ROC curves were plotted to display system performance and identify thresholds for match determination. In addition, system performance as a function of ambient light intensity was tested. Results Using optimized parameters in the matching algorithm, the sensitivity of the system for 5299 trials was 96.5% and the specificity was 96.7%. The results indicate a fairly robust methodology for verifying, in real-time, a specific face through comparison from a precollected reference data set. In its current implementation, the process of data collection for each face and subsequent matching session averaged approximately 30 s, which may be too onerous to provide a realistic supplement to patient identification in a clinical setting. Despite the time commitment, the data collection process was well tolerated by all participants and most robust when consistent ambient light conditions were maintained across both the reference recording session and subsequent real-time identification sessions. Conclusion A Facial Recognition system can be implemented for patient identification using the Microsoft Kinect v2 sensor and the distributed SDK. In its present form, the system is accurate—if time consuming—and further iterations of the method could provide a robust, easy to implement, and cost-effective supplement to traditional patient identification methods.
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mo fg campus tep1 02 Facial Recognition and recall using kinect v2 for patient verification
Medical Physics, 2016Co-Authors: Evan Silverstein, M SnyderAbstract:Purpose: Investigate capability and accuracy of the Kinect v2 camera for Facial Recognition to use as a tool for patient check-in upon arrival, as well as patient verification when entering treatment vault. Methods: The Kinect software has a native Facial Recognition feature which recognizes major Facial landmarks (eyes, nose, mouth, etc). 1347 Facial vertices are automatically generated, each located at a specific Facial point at standardized distances between points. Through a process of Facial contour mapping, the vertices are modified such that their coordinates are specific to the imaged face. 35 of these vertices are labeled with specific landmarks on the face (Nose Tip, Right Cheek Center, Left Eye Mid Top, etc).Using code written in C#, 595 3D vectors are calculated utilizing the 35 native modified vertices. For each initial acquisition, the vector magnitudes are saved to file. For each comparison acquisition, the difference of identical vector magnitudes is calculated between the current face and each face saved to file. The mean of all magnitude differences per face is taken as the determining factor for Facial match with the smallest mean value between faces determining the correct match. A database 15 imaged faces was used for comparison. Initial acquisitions kept Pitch, Yaw, and Roll between −10 and 10 degrees and distance between 90–100 cm. Results: Through 25 acquisitions varying Pitch, Yaw, and Roll between −10 to 10 degrees as well 50 acquisitions modifying the distance up to 300 cm for two different test faces, the program obtained 100% true positives. Conclusion: This Facial Recognition and recall program has excellent potential to function as a clinical tool for patient verification and check-in given these results. Further study is necessary for acquisition of a sufficient database to ensure an acceptable true positive rate with increased population size.
Evan Silverstein - One of the best experts on this subject based on the ideXlab platform.
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implementation of Facial Recognition with microsoft kinect v2 sensor for patient verification
Medical Physics, 2017Co-Authors: Evan Silverstein, M SnyderAbstract:Purpose The aim of this study was to present a straightforward implementation of Facial Recognition using the Microsoft Kinect v2 sensor for patient identification in a radiotherapy setting. Materials and methods A Facial Recognition system was created with the Microsoft Kinect v2 using a Facial mapping library distributed with the Kinect v2 SDK as a basis for the algorithm. The system extracts 31 fiducial points representing various Facial landmarks which are used in both the creation of a reference data set and subsequent evaluations of real-time sensor data in the matching algorithm. To test the algorithm, a database of 39 faces was created, each with 465 vectors derived from the fiducial points, and a one-to-one matching procedure was performed to obtain sensitivity and specificity data of the Facial identification system. ROC curves were plotted to display system performance and identify thresholds for match determination. In addition, system performance as a function of ambient light intensity was tested. Results Using optimized parameters in the matching algorithm, the sensitivity of the system for 5299 trials was 96.5% and the specificity was 96.7%. The results indicate a fairly robust methodology for verifying, in real-time, a specific face through comparison from a precollected reference data set. In its current implementation, the process of data collection for each face and subsequent matching session averaged approximately 30 s, which may be too onerous to provide a realistic supplement to patient identification in a clinical setting. Despite the time commitment, the data collection process was well tolerated by all participants and most robust when consistent ambient light conditions were maintained across both the reference recording session and subsequent real-time identification sessions. Conclusion A Facial Recognition system can be implemented for patient identification using the Microsoft Kinect v2 sensor and the distributed SDK. In its present form, the system is accurate—if time consuming—and further iterations of the method could provide a robust, easy to implement, and cost-effective supplement to traditional patient identification methods.
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mo fg campus tep1 02 Facial Recognition and recall using kinect v2 for patient verification
Medical Physics, 2016Co-Authors: Evan Silverstein, M SnyderAbstract:Purpose: Investigate capability and accuracy of the Kinect v2 camera for Facial Recognition to use as a tool for patient check-in upon arrival, as well as patient verification when entering treatment vault. Methods: The Kinect software has a native Facial Recognition feature which recognizes major Facial landmarks (eyes, nose, mouth, etc). 1347 Facial vertices are automatically generated, each located at a specific Facial point at standardized distances between points. Through a process of Facial contour mapping, the vertices are modified such that their coordinates are specific to the imaged face. 35 of these vertices are labeled with specific landmarks on the face (Nose Tip, Right Cheek Center, Left Eye Mid Top, etc).Using code written in C#, 595 3D vectors are calculated utilizing the 35 native modified vertices. For each initial acquisition, the vector magnitudes are saved to file. For each comparison acquisition, the difference of identical vector magnitudes is calculated between the current face and each face saved to file. The mean of all magnitude differences per face is taken as the determining factor for Facial match with the smallest mean value between faces determining the correct match. A database 15 imaged faces was used for comparison. Initial acquisitions kept Pitch, Yaw, and Roll between −10 and 10 degrees and distance between 90–100 cm. Results: Through 25 acquisitions varying Pitch, Yaw, and Roll between −10 to 10 degrees as well 50 acquisitions modifying the distance up to 300 cm for two different test faces, the program obtained 100% true positives. Conclusion: This Facial Recognition and recall program has excellent potential to function as a clinical tool for patient verification and check-in given these results. Further study is necessary for acquisition of a sufficient database to ensure an acceptable true positive rate with increased population size.
Sos S Agaian - One of the best experts on this subject based on the ideXlab platform.
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autonomous Facial Recognition system inspired by human visual system based logarithmical image visualization technique
Mobile Multimedia Image Processing Security and Applications 2017, 2017Co-Authors: Qianwen Wan, Karen Panetta, Sos S AgaianAbstract:Autonomous Facial Recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and Facial expressions can impair the performance of Recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous Facial Recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for Facial Recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method’s efficiency, accuracy, and robustness of illumination invariance for Facial Recognition.
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autonomous Facial Recognition based on the human visual system
International Conference on Imaging Systems and Techniques, 2015Co-Authors: Qianwen Wan, Karen Panetta, Sos S AgaianAbstract:This paper presents a real-time Facial Recognition system utilizing our human visual system algorithms coupled with logarithm Logical Binary Pattern feature descriptors and our region weighted model. The architecture can quickly find and rank the closest matches of a test image to a database of stored images. There are many potential applications for this work, including homeland security applications such as identifying persons of interest and other robot vision applications such as search and rescue missions. This new method significantly improves the performance of the previous Local Binary Pattern method. For our prototype application, we supplied the system testing images and found their best matches in the database of training images. In addition, the results were further improved by weighting the contribution of the most distinctive Facial features. The system evaluates and selects the best matching image using the chi-squared statistic.
Anna C. E. Hurst - One of the best experts on this subject based on the ideXlab platform.
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Facial Recognition software in clinical dysmorphology.
Current opinion in pediatrics, 2018Co-Authors: Anna C. E. HurstAbstract:PURPOSE OF REVIEW The current review aims to discuss the incorporation of Facial Recognition software into the clinical practice of dysmorphology and medical genetics. RECENT FINDINGS Facial Recognition software has improved the process of generating a differential diagnosis for rare genetic syndromes, and recent publications demonstrate utility in both research and clinical applications. Software programs are freely available to verified medical providers and can be incorporated into routine clinic encounters. SUMMARY As Facial Recognition software capabilities improve, two-dimensional image capture with artificial intelligence interpretation may become a useful tool within many areas of medicine. Geneticists and researchers can use such software to enhance their differential diagnoses, to study similarities and differences between patient cohorts, and to improve the interpretation of genomic data. Pediatricians and subspecialists may use tools to identify patients who may benefit from a genetic evaluation, and educators can use these tools to interest students in the study of dysmorphoplogy and genetic syndromes.
