Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126368
Chao Shen, Shichao Pei, Tianwen Yu, X. Guan
A wealth of sensors on smartphone has greatly enriched people's life, but these sensors also brought potential security problems since they allow third-party applications to monitor the motion changes of smartphones. This paper presents an empirical study of analyzing the characteristics of accelerometer and magnetometer data collected from third-party applications to infer user inputs on smartphone. Specifically, an installed application was run as a background process to monitor the data of motion sensors. Accelerometer data was analyzed to detect the occurrence of touch tap actions. Then the accelerometer data and magnetometer data were combined together to build a model for inferring the tap position on touchscreen. Along with common layouts of keyboard or number pad, one can easily obtain users' inputs. Results indicated that users' inputs could be accurately inferred from the data of motion sensors, with the accuracies of 100% and 80% for tap-action detection and input inference in some cases. We conclude that readings from motion sensor are a powerful side channel for inferring user inputs, and could provide extra avenues for attackers.
{"title":"On motion sensors as source for user input inference in smartphones","authors":"Chao Shen, Shichao Pei, Tianwen Yu, X. Guan","doi":"10.1109/ISBA.2015.7126368","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126368","url":null,"abstract":"A wealth of sensors on smartphone has greatly enriched people's life, but these sensors also brought potential security problems since they allow third-party applications to monitor the motion changes of smartphones. This paper presents an empirical study of analyzing the characteristics of accelerometer and magnetometer data collected from third-party applications to infer user inputs on smartphone. Specifically, an installed application was run as a background process to monitor the data of motion sensors. Accelerometer data was analyzed to detect the occurrence of touch tap actions. Then the accelerometer data and magnetometer data were combined together to build a model for inferring the tap position on touchscreen. Along with common layouts of keyboard or number pad, one can easily obtain users' inputs. Results indicated that users' inputs could be accurately inferred from the data of motion sensors, with the accuracies of 100% and 80% for tap-action detection and input inference in some cases. We conclude that readings from motion sensor are a powerful side channel for inferring user inputs, and could provide extra avenues for attackers.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"75 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114034573","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}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126350
Dong Cao, R. He, Man Zhang, Zhenan Sun, T. Tan
This paper presents a new approach for real-world gender recognition, where images are captured under uncontrolled environments with various poses, illuminations and expressions. While a large number of gender recognition methods have been introduced in recent years, most of them describe each image in a single feature space or simple combination of multiple individual spaces, which can not be powerful enough to alleviate the noise in real-world scenarios. To address this, we propose exploring multiple order local binary patterns (MOLBP) as features for learning, and develop a localized multi-boost learning (LMBL) algorithm to combine the different features for classification. Experimental results show that the proposed algorithm outperforms state-of-the-art methods in two real-world datasets.
{"title":"Real-world gender recognition using multi-order LBP and localized multi-boost learning","authors":"Dong Cao, R. He, Man Zhang, Zhenan Sun, T. Tan","doi":"10.1109/ISBA.2015.7126350","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126350","url":null,"abstract":"This paper presents a new approach for real-world gender recognition, where images are captured under uncontrolled environments with various poses, illuminations and expressions. While a large number of gender recognition methods have been introduced in recent years, most of them describe each image in a single feature space or simple combination of multiple individual spaces, which can not be powerful enough to alleviate the noise in real-world scenarios. To address this, we propose exploring multiple order local binary patterns (MOLBP) as features for learning, and develop a localized multi-boost learning (LMBL) algorithm to combine the different features for classification. Experimental results show that the proposed algorithm outperforms state-of-the-art methods in two real-world datasets.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125236243","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}
The performance of iris recognition reduces when the images are captured at a distance. However, such images generally contain periocular region which can be utilized for person recognition. In this research, we propose a novel context switching algorithm that dynamically selects the best descriptor for color iris and periocular regions. Using predefined protocols, the performance of the proposed algorithm is evaluated on UBIRIS V2 and FRGC datasets, and the results show improved performance compared to existing algorithms.
{"title":"Person identification at a distance via ocular biometrics","authors":"Aishwarya Jain, Paritosh Mittal, Gaurav Goswami, Mayank Vatsa, Richa Singh","doi":"10.1109/ISBA.2015.7126353","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126353","url":null,"abstract":"The performance of iris recognition reduces when the images are captured at a distance. However, such images generally contain periocular region which can be utilized for person recognition. In this research, we propose a novel context switching algorithm that dynamically selects the best descriptor for color iris and periocular regions. Using predefined protocols, the performance of the proposed algorithm is evaluated on UBIRIS V2 and FRGC datasets, and the results show improved performance compared to existing algorithms.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126692156","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}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126359
Maria V. Ruiz-Blondet, Sarah Laszlo, Zhanpeng Jin
Electrical brain activities can be measured noninvasively using electroencephalogram (EEG). This electric signal changes for different tasks, and also changes from subject to subject. Previous studies have shown that the EEG signal is unique enough to be used as a biometric characteristic. However, it is well known that the brain activity can change according to our emotion or stress status, among many other factors. The stability of EEG signals as a biometric has not yet been well explored and understood. In this work, we explicitly investigated and assessed the permanence of the non-volitional EEG brainwaves over the course of time. Specifically, we analyzed how much the EEG signal changes over a period of six months, since any drastic change would make it unusable as an authentication method. The results are very encouraging, yielding high accuracy throughout the six-month period.
{"title":"Assessment of permanence of non-volitional EEG brainwaves as a biometric","authors":"Maria V. Ruiz-Blondet, Sarah Laszlo, Zhanpeng Jin","doi":"10.1109/ISBA.2015.7126359","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126359","url":null,"abstract":"Electrical brain activities can be measured noninvasively using electroencephalogram (EEG). This electric signal changes for different tasks, and also changes from subject to subject. Previous studies have shown that the EEG signal is unique enough to be used as a biometric characteristic. However, it is well known that the brain activity can change according to our emotion or stress status, among many other factors. The stability of EEG signals as a biometric has not yet been well explored and understood. In this work, we explicitly investigated and assessed the permanence of the non-volitional EEG brainwaves over the course of time. Specifically, we analyzed how much the EEG signal changes over a period of six months, since any drastic change would make it unusable as an authentication method. The results are very encouraging, yielding high accuracy throughout the six-month period.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130676522","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}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126349
Xiang Li, Ancong Wu, Mei Cao, Jinjie You, Weishi Zheng
Person re-identification is an important problem of matching persons across non-overlapping camera views. However, the re-identification is still far from achieving reliable matching. First, many existing approaches are wholebody- based matching, and how body parts could affect and assist the matching is still not clearly known. Second, the learned similarity measurement/metric is equally used for each pair of probe and gallery images, and the bias of the measurement is not considered. In this paper, we address the above two problems in order to conduct a more reliable matching. More specifically, we propose a reliable integrated matching scheme (IMS), which uses body parts to assist matching of the whole body. Moreover, a sparsity-based confidence is also presented for regulating the learned metric to improve the matching reliability. The experiments conducted on three publicly available datasets confirm that the proposed scheme is effective for person re-identification.
{"title":"Towards more reliable matching for person re-identification","authors":"Xiang Li, Ancong Wu, Mei Cao, Jinjie You, Weishi Zheng","doi":"10.1109/ISBA.2015.7126349","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126349","url":null,"abstract":"Person re-identification is an important problem of matching persons across non-overlapping camera views. However, the re-identification is still far from achieving reliable matching. First, many existing approaches are wholebody- based matching, and how body parts could affect and assist the matching is still not clearly known. Second, the learned similarity measurement/metric is equally used for each pair of probe and gallery images, and the bias of the measurement is not considered. In this paper, we address the above two problems in order to conduct a more reliable matching. More specifically, we propose a reliable integrated matching scheme (IMS), which uses body parts to assist matching of the whole body. Moreover, a sparsity-based confidence is also presented for regulating the learned metric to improve the matching reliability. The experiments conducted on three publicly available datasets confirm that the proposed scheme is effective for person re-identification.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133352916","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}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126367
Zhigang Yao, B. Vibert, C. Charrier, Christophe Rosenberger
The embedded applications of fingerprint proposed so far are chiefly based on the minutiae template. This kind of system is not resource-free and minutiae template is generally sacrificed to cover the shortage. This paper presents several simple yet efficient no-image minutiae selection approaches (NIMS) for the standard minutiae templates (ISO/IEC 19794-2). With the reduced-templates obtained by using the proposed methods, the overall performance can be guaranteed in comparing with the results generated by the original templates. The interoperability tests are performed with several FVC databases. An additional analysis with the quality of the enrollment samples is also carried out. The experimental results demonstrate the validity and efficiency of the proposed approaches.
{"title":"Blind minutiae selection for standard minutiae templates","authors":"Zhigang Yao, B. Vibert, C. Charrier, Christophe Rosenberger","doi":"10.1109/ISBA.2015.7126367","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126367","url":null,"abstract":"The embedded applications of fingerprint proposed so far are chiefly based on the minutiae template. This kind of system is not resource-free and minutiae template is generally sacrificed to cover the shortage. This paper presents several simple yet efficient no-image minutiae selection approaches (NIMS) for the standard minutiae templates (ISO/IEC 19794-2). With the reduced-templates obtained by using the proposed methods, the overall performance can be guaranteed in comparing with the results generated by the original templates. The interoperability tests are performed with several FVC databases. An additional analysis with the quality of the enrollment samples is also carried out. The experimental results demonstrate the validity and efficiency of the proposed approaches.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122782832","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}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126348
Kun Zhao, A. Wiliem, B. Lovell
Video surveillance systems require both accurate and efficient operations for biometric classification tasks. Recent research has shown that modelling video data on manifold space leads to significant improvement on classification accuracy. However, processing manifold points directly often requires computationally expensive operations since manifolds are non-Euclidean. In this work, we tackle this problem by projecting the manifold points into a random projection space constructed by orthonormal hyperplanes. As the projection notion in manifold space is generally not well defined, the random projection is done indirectly via the Reproducing Kernel Hilbert Space (RKHS). There are at least two reasons that make random projection for manifold points attractive: (1) by random projection, manifold points can be projected into lower dimensional space while preserving most of the structure in the RKHS; and (2) after random projection, the classification of manifold points can be solved via scalable linear classifiers. Our formulation is novel compared to the previous work in the way that we use an orthogonality constraint in the hyperplane generation. By orthogonalising the hyperplanes, the mutual information between the dimensions in the projected space is maximised; a desirable property for addressing classification problems. Experimental results in two biometric applications such as action and gait-based gender recognition, show that we can achieve better accuracy than the state-of-the-art random projection method for manifold points. Further, comparisons with kernelised classifiers show that our method achieves nearly 3-fold speed up on average whilst maintaining the accuracy.
{"title":"Kernelised orthonormal random projection on grassmann manifolds with applications to action and gait-based gender recognition","authors":"Kun Zhao, A. Wiliem, B. Lovell","doi":"10.1109/ISBA.2015.7126348","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126348","url":null,"abstract":"Video surveillance systems require both accurate and efficient operations for biometric classification tasks. Recent research has shown that modelling video data on manifold space leads to significant improvement on classification accuracy. However, processing manifold points directly often requires computationally expensive operations since manifolds are non-Euclidean. In this work, we tackle this problem by projecting the manifold points into a random projection space constructed by orthonormal hyperplanes. As the projection notion in manifold space is generally not well defined, the random projection is done indirectly via the Reproducing Kernel Hilbert Space (RKHS). There are at least two reasons that make random projection for manifold points attractive: (1) by random projection, manifold points can be projected into lower dimensional space while preserving most of the structure in the RKHS; and (2) after random projection, the classification of manifold points can be solved via scalable linear classifiers. Our formulation is novel compared to the previous work in the way that we use an orthogonality constraint in the hyperplane generation. By orthogonalising the hyperplanes, the mutual information between the dimensions in the projected space is maximised; a desirable property for addressing classification problems. Experimental results in two biometric applications such as action and gait-based gender recognition, show that we can achieve better accuracy than the state-of-the-art random projection method for manifold points. Further, comparisons with kernelised classifiers show that our method achieves nearly 3-fold speed up on average whilst maintaining the accuracy.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123575610","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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