Y. Iwashita, Shuhei Takaki, K. Morooka, Tokuo Tsuji, R. Kurazume
Intelligent visual surveillance, which relies heavily on human motion detection / recognition and people recognition, has received a lot of attention for its use in effective monitoring of public places. However, there is a concern of loss of privacy due to distinguishable facial and body information. To deal with this issue, researchers proposed to protect privacy example by filtering of face or body areas, and developed methods of people identification from videos in which people's faces has been obfuscated, masked by digital filters. Along the same line of research dealing with videos in which the people faces were masked by filters, this paper introduces a method to detect abnormal behavior. In the proposed method, we first mask face areas in videos by Multiple Instance Learning tracking, and extract silhouette area from each image. We then extract features using affine moment invariants, and perform classification. We build a database including normal and abnormal behaviors, and we show the effectiveness of the proposed method on cases from the database.
{"title":"Abnormal Behavior Detection Using Privacy Protected Videos","authors":"Y. Iwashita, Shuhei Takaki, K. Morooka, Tokuo Tsuji, R. Kurazume","doi":"10.1109/EST.2013.16","DOIUrl":"https://doi.org/10.1109/EST.2013.16","url":null,"abstract":"Intelligent visual surveillance, which relies heavily on human motion detection / recognition and people recognition, has received a lot of attention for its use in effective monitoring of public places. However, there is a concern of loss of privacy due to distinguishable facial and body information. To deal with this issue, researchers proposed to protect privacy example by filtering of face or body areas, and developed methods of people identification from videos in which people's faces has been obfuscated, masked by digital filters. Along the same line of research dealing with videos in which the people faces were masked by filters, this paper introduces a method to detect abnormal behavior. In the proposed method, we first mask face areas in videos by Multiple Instance Learning tracking, and extract silhouette area from each image. We then extract features using affine moment invariants, and perform classification. We build a database including normal and abnormal behaviors, and we show the effectiveness of the proposed method on cases from the database.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129372047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most of the reported algorithms for facial expression recognition (FER) are based on visible expression databases. However, visible images are affected by illumination variations which can cause significant disparities in image appearance and texture. In this paper preliminary results of a new FER algorithm, using Gauss-Laguerre (GL) filter of circular harmonic wavelets to extract features for infrared images, are presented. By using GL filters with properly tuned-parameters, it is possible to generate a set of redundant wavelets that enable an accurate extraction of complex texture features from an infrared image. In addition, we utilize GL filters that are highly suitable for FER in visible images. The combination of infrared and visible FER using common feature extraction approach saves time and reduces the complexity in a multiple-sensors scenario. K-nearest neighbor is used for classification on OTCBVS and USTC-NVIE databases. The results show effective performance for using GL filters in FER for infrared images.
{"title":"An Efficient Facial Expression Recognition System in Infrared Images","authors":"A. Poursaberi, S. Yanushkevich, M. Gavrilova","doi":"10.1109/EST.2013.11","DOIUrl":"https://doi.org/10.1109/EST.2013.11","url":null,"abstract":"Most of the reported algorithms for facial expression recognition (FER) are based on visible expression databases. However, visible images are affected by illumination variations which can cause significant disparities in image appearance and texture. In this paper preliminary results of a new FER algorithm, using Gauss-Laguerre (GL) filter of circular harmonic wavelets to extract features for infrared images, are presented. By using GL filters with properly tuned-parameters, it is possible to generate a set of redundant wavelets that enable an accurate extraction of complex texture features from an infrared image. In addition, we utilize GL filters that are highly suitable for FER in visible images. The combination of infrared and visible FER using common feature extraction approach saves time and reduces the complexity in a multiple-sensors scenario. K-nearest neighbor is used for classification on OTCBVS and USTC-NVIE databases. The results show effective performance for using GL filters in FER for infrared images.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131467321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
EEG signals, measuring transient brain activities, can be used as a source of biometric information with potential application in high-security person recognition scenarios. However, due to the inherent nature of these signals and the process used for their acquisition, their effective preprocessing is critical for their successful utilisation. In this paper we compare the effectiveness of different wavelet-based noise removal methods and propose an EEG-based biometric identification system which combines two such de-noising methods to enhance the signal preprocessing stage. In tests using 50 subjects from a public database, the proposed new approach is shown to provide improved identification performance over alternative techniques. Another important preprocessing consideration is the segmentation of the EEG record prior to de-noising. Different segmentation approaches were investigated and the trade-off between performance and computation time is explored. Finally the paper reports on the impact of the choice of wavelet function used for feature extraction on system performance.
{"title":"Wavelet-Based EEG Preprocessing for Biometric Applications","authors":"Su Yang, F. Deravi","doi":"10.1109/EST.2013.14","DOIUrl":"https://doi.org/10.1109/EST.2013.14","url":null,"abstract":"EEG signals, measuring transient brain activities, can be used as a source of biometric information with potential application in high-security person recognition scenarios. However, due to the inherent nature of these signals and the process used for their acquisition, their effective preprocessing is critical for their successful utilisation. In this paper we compare the effectiveness of different wavelet-based noise removal methods and propose an EEG-based biometric identification system which combines two such de-noising methods to enhance the signal preprocessing stage. In tests using 50 subjects from a public database, the proposed new approach is shown to provide improved identification performance over alternative techniques. Another important preprocessing consideration is the segmentation of the EEG record prior to de-noising. Different segmentation approaches were investigated and the trade-off between performance and computation time is explored. Finally the paper reports on the impact of the choice of wavelet function used for feature extraction on system performance.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134395350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Gillham, G. Howells, S. Spurgeon, Stephen Kelly, M. Pepper
Powered wheelchair users may find operation in enclosed environments such as buildings difficult, a fundamental problem exists: wheelchairs are not much narrower than the doorway they wish to pass through. The ability to detect and pass through doorways represents a major current challenge for automated guided wheelchairs. We utilize a simple doorway pattern recognition technique for fast processing in a real-time system for robotic wheelchair users. We are able to show a 96% detection and identification of 5 individual doorways and an 86% recognition rate of 22 separate approach angles and translations. We conclude that pattern recognition using features obtained from simple constrained infrared ranging sensor data binning can be utilized for fast identification of doorways, and important coarse position and approach angle determination, suitable for real-time trajectory adjustment, representing a significant enhancement in this area.
{"title":"Real-Time Doorway Detection and Alignment Determination for Improved Trajectory Generation in Assistive Mobile Robotic Wheelchairs","authors":"M. Gillham, G. Howells, S. Spurgeon, Stephen Kelly, M. Pepper","doi":"10.1109/EST.2013.18","DOIUrl":"https://doi.org/10.1109/EST.2013.18","url":null,"abstract":"Powered wheelchair users may find operation in enclosed environments such as buildings difficult, a fundamental problem exists: wheelchairs are not much narrower than the doorway they wish to pass through. The ability to detect and pass through doorways represents a major current challenge for automated guided wheelchairs. We utilize a simple doorway pattern recognition technique for fast processing in a real-time system for robotic wheelchair users. We are able to show a 96% detection and identification of 5 individual doorways and an 86% recognition rate of 22 separate approach angles and translations. We conclude that pattern recognition using features obtained from simple constrained infrared ranging sensor data binning can be utilized for fast identification of doorways, and important coarse position and approach angle determination, suitable for real-time trajectory adjustment, representing a significant enhancement in this area.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134444433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates some practical aspects of the employment of measurable features derived from characteristics of given Cloud computing servers for the generation of encryption keys pertaining to the servers, a technique termed ICmetric. The ICmetric technology requires identifying the suitable features in a distributed environment for encryption purpose. Ideally, the nature of the features should be identical for all of the systems considered, while the values of these features should allow for unique identification of each of the system servers. This paper looks at the properties of the server behaviors as a potential ICmetric feature, and explores how the number of its samples being inputted into the ICmetric system affects stability of the system's performance.
{"title":"Investigation of Properties of ICmetric in Cloud","authors":"Bin Ye, G. Howells, Mustafa Haciosman","doi":"10.1109/EST.2013.36","DOIUrl":"https://doi.org/10.1109/EST.2013.36","url":null,"abstract":"This paper investigates some practical aspects of the employment of measurable features derived from characteristics of given Cloud computing servers for the generation of encryption keys pertaining to the servers, a technique termed ICmetric. The ICmetric technology requires identifying the suitable features in a distributed environment for encryption purpose. Ideally, the nature of the features should be identical for all of the systems considered, while the values of these features should allow for unique identification of each of the system servers. This paper looks at the properties of the server behaviors as a potential ICmetric feature, and explores how the number of its samples being inputted into the ICmetric system affects stability of the system's performance.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125936158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Ragot, F. Bouzbouz, R. Khemmar, J. Ertaud, Annemarie M. Kökösy, O. Labbani-Igbida, Patricia Sajous, Emmnanuel Niyonsaba, Daniel Reguer, Huosheng Hu, K. Mcdonald-Maier, K. Sirlantzis, G. Howells, M. Pepper, M. Sakel
Europe faces a major and growing healthcare problem due to increase in population, increasing longevity and an aging population with disability. Such dependent, elderly, disabled and vulnerable persons, are concerned since they wish to live at home as long as possible. This aspiration is also shared by national policies and communities across EU. To ensure the optimum care of dependent people, innovative solutions are encouraged to maintain independent life style. This paper outlines two projects, SYSIASS and COALAS, which aim to develop a set of technology based solutions to meet the needs and empower these people by enhancing mobility and communication.
{"title":"Enhancing the Autonomy of Disabled Persons: Assistive Technologies Directed by User Feedback","authors":"N. Ragot, F. Bouzbouz, R. Khemmar, J. Ertaud, Annemarie M. Kökösy, O. Labbani-Igbida, Patricia Sajous, Emmnanuel Niyonsaba, Daniel Reguer, Huosheng Hu, K. Mcdonald-Maier, K. Sirlantzis, G. Howells, M. Pepper, M. Sakel","doi":"10.1109/EST.2013.20","DOIUrl":"https://doi.org/10.1109/EST.2013.20","url":null,"abstract":"Europe faces a major and growing healthcare problem due to increase in population, increasing longevity and an aging population with disability. Such dependent, elderly, disabled and vulnerable persons, are concerned since they wish to live at home as long as possible. This aspiration is also shared by national policies and communities across EU. To ensure the optimum care of dependent people, innovative solutions are encouraged to maintain independent life style. This paper outlines two projects, SYSIASS and COALAS, which aim to develop a set of technology based solutions to meet the needs and empower these people by enhancing mobility and communication.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133083525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shoko Miyauchi, K. Morooka, Yasushi Miyagi, Takaichi Fukuda, Tokuo Tsuji, R. Kurazume
This paper proposes a new method for mapping a tissue surface model onto an arbitrary target surface while preserving the geometrical features of the tissue surface. In our method, firstly, the tissue model is roughly deformed by using Self-organizing Deformable Model. Since the deformed model may contain folded patches, the folded patches are removed. Moreover, by Free-Form Deformation (FFD), and the area- and angle-preserving mapping, the model is mapped onto the target surface while preserving geometrical properties of the original model. From several experimental results, we can conclude that the proposed method can map tissue models onto arbitrary target surface without foldovers.
{"title":"Tissue Surface Model Mapping onto Arbitrary Target Surface Based on Self-Organizing Deformable Model","authors":"Shoko Miyauchi, K. Morooka, Yasushi Miyagi, Takaichi Fukuda, Tokuo Tsuji, R. Kurazume","doi":"10.1109/EST.2013.34","DOIUrl":"https://doi.org/10.1109/EST.2013.34","url":null,"abstract":"This paper proposes a new method for mapping a tissue surface model onto an arbitrary target surface while preserving the geometrical features of the tissue surface. In our method, firstly, the tissue model is roughly deformed by using Self-organizing Deformable Model. Since the deformed model may contain folded patches, the folded patches are removed. Moreover, by Free-Form Deformation (FFD), and the area- and angle-preserving mapping, the model is mapped onto the target surface while preserving geometrical properties of the original model. From several experimental results, we can conclude that the proposed method can map tissue models onto arbitrary target surface without foldovers.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125780764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Polynomial regression is still widely used in engineering and economics where polynomials of low order (usually less than tenth order) are being fitted to experimental data. However, the fundamental problem of selecting the optimal order of the polynomial to be fitted to experimental data is not a straightforward problem. This paper investigates the performance of automated methods for predicting the order of the polynomial that can be fitted on the decision boundary formed between two classes in a pattern recognition problem. We have investigated statistical methods and proposed a method of predicting the order of the polynomial. Our proposed machine learning method is computing a number of measurements on the input data which are used by a classifier trained offline to predict the order of the polynomial that should be fitted to the decision boundary. We have considered two matching scenarios. One scenario is where we have counted only the exact matches as being correct and another scenario in which we count as correct an exact match and higher polynomial orders. Experimental results on synthetic data show that our proposed method predicts the exact order of the polynomial with 31.90% accuracy as opposed to 13.22% of the best statistical method, but it also under-estimates the true order almost twice as often when compared to statistical methods of predicting the order of the polynomial to be fitted to the same data points.
{"title":"Polynomial Order Prediction Using a Classifier Trained on Meta-Measurements","authors":"B. Gherman, K. Sirlantzis","doi":"10.1109/EST.2013.26","DOIUrl":"https://doi.org/10.1109/EST.2013.26","url":null,"abstract":"Polynomial regression is still widely used in engineering and economics where polynomials of low order (usually less than tenth order) are being fitted to experimental data. However, the fundamental problem of selecting the optimal order of the polynomial to be fitted to experimental data is not a straightforward problem. This paper investigates the performance of automated methods for predicting the order of the polynomial that can be fitted on the decision boundary formed between two classes in a pattern recognition problem. We have investigated statistical methods and proposed a method of predicting the order of the polynomial. Our proposed machine learning method is computing a number of measurements on the input data which are used by a classifier trained offline to predict the order of the polynomial that should be fitted to the decision boundary. We have considered two matching scenarios. One scenario is where we have counted only the exact matches as being correct and another scenario in which we count as correct an exact match and higher polynomial orders. Experimental results on synthetic data show that our proposed method predicts the exact order of the polynomial with 31.90% accuracy as opposed to 13.22% of the best statistical method, but it also under-estimates the true order almost twice as often when compared to statistical methods of predicting the order of the polynomial to be fitted to the same data points.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130388757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper is concerned with the design of a visual module, which employs a novel Principal Components Analysis (PCA) based algorithm to fuse collected multi-focus images. The data transmission is performed using wireless communication. Simulation results are presented which illustrate the performance of the algorithm and confirm its suitability for implementation in a resource-constrained embedded system.
{"title":"Image Fusion for an EOD Robot Wireless Visual Module","authors":"Hongyuan Jing, T. Vladimirova","doi":"10.1109/EST.2013.28","DOIUrl":"https://doi.org/10.1109/EST.2013.28","url":null,"abstract":"This paper is concerned with the design of a visual module, which employs a novel Principal Components Analysis (PCA) based algorithm to fuse collected multi-focus images. The data transmission is performed using wireless communication. Simulation results are presented which illustrate the performance of the algorithm and confirm its suitability for implementation in a resource-constrained embedded system.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127581288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Technologies of cancelable biometrics protect bio- metric data by applying transforms to biometric signals which provide a comparison of biometric templates in the transformed domain. Approaches to cancelable biometrics entail a significant decrease in recognition accuracy, i.e. applied feature transforms highly effect biometric feature extractors and comparators. In the presented work cancelable iris biometric systems operating in image domain are investigated. Sequences of comparison scores obtained during the process of feature alignment are combined in various fusion scenarios. As a result recognition accuracy is significantly improved, eliminating the inevitable decrease in accuracy caused by cancelable transforms, i.e. the proposed approach enables an operation of cancelable iris biometric systems at a high security level.
{"title":"Comparison Score Fusion Towards an Optimal Alignment for Enhancing Cancelable Iris Biometrics","authors":"C. Rathgeb, C. Busch","doi":"10.1109/EST.2013.39","DOIUrl":"https://doi.org/10.1109/EST.2013.39","url":null,"abstract":"Technologies of cancelable biometrics protect bio- metric data by applying transforms to biometric signals which provide a comparison of biometric templates in the transformed domain. Approaches to cancelable biometrics entail a significant decrease in recognition accuracy, i.e. applied feature transforms highly effect biometric feature extractors and comparators. In the presented work cancelable iris biometric systems operating in image domain are investigated. Sequences of comparison scores obtained during the process of feature alignment are combined in various fusion scenarios. As a result recognition accuracy is significantly improved, eliminating the inevitable decrease in accuracy caused by cancelable transforms, i.e. the proposed approach enables an operation of cancelable iris biometric systems at a high security level.","PeriodicalId":213735,"journal":{"name":"2013 Fourth International Conference on Emerging Security Technologies","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133412181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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