2009 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第3页

Cancelable iris biometrics and using Error Correcting Codes to reduce variability in biometric data 可取消虹膜生物特征和使用纠错码来减少生物特征数据的可变性

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206646

S. Kanade, D. Petrovska-Delacrétaz, B. Dorizzi

{"title":"Cancelable iris biometrics and using Error Correcting Codes to reduce variability in biometric data","authors":"S. Kanade, D. Petrovska-Delacrétaz, B. Dorizzi","doi":"10.1109/CVPR.2009.5206646","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206646","url":null,"abstract":"With the increasing use of biometrics, more and more concerns are being raised about the privacy of the personal biometric data. Conventional biometric systems store biometric templates in a database. This may lead to the possibility of tracking personal information stored in one database by getting access to another database through cross-database matching. Moreover, biometric data are permanently associated with the user. Hence if stolen, they are lost permanently and become unusable in that system and possibly in all other systems based on that biometrics. In order to overcome this non-revocability of biometrics, we propose a two factor scheme to generate cancelable iris templates using iris-biometric and password. We employ a user specific shuffling key to shuffle the iris codes. Additionally, we introduce a novel way to use error correcting codes (ECC) to reduce the variabilities in biometric data. The shuffling scheme increases the impostor Hamming distance leaving genuine Hamming distance intact while the ECC reduce the Hamming distance for genuine comparisons by a larger amount than for the impostor comparisons. This results in better separation between genuine and impostor users which improves the verification performance. The shuffling key is protected by a password which makes the system truly revocable. The biometric data is stored in a protected form which protects the privacy. The proposed scheme reduces the equal error rate (EER) of the system by more than 90% (e.g., from 1.70% to 0.057% on the NIST-ICE database).","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126695730","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}

引用次数: 86

On edge detection on surfaces 关于曲面的边缘检测

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206517

Michael Kolomenkin, I. Shimshoni, A. Tal

引用次数: 72

View-invariant dynamic texture recognition using a bag of dynamical systems 使用一组动态系统的视不变动态纹理识别

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206847

Avinash Ravichandran, Rizwan Ahmed Chaudhry, R. Vidal

引用次数: 132

Real-time learning of accurate patch rectification 实时学习准确的补丁整改

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206794

Stefan Hinterstoißer, Oliver Kutter, Nassir Navab, P. Fua, V. Lepetit

引用次数: 34

Removing partial blur in a single image 去除单个图像中的部分模糊

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206625

Shengyang Dai, Ying Wu

引用次数: 59

A unified model of specular and diffuse reflectance for rough, glossy surfaces 粗糙光滑表面的镜面反射和漫反射的统一模型

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206498

W. Smith, E. Hancock

引用次数: 2

Shape priors and discrete MRFs for knowledge-based segmentation 基于知识分割的形状先验和离散mrf

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206649

A. Besbes, N. Komodakis, G. Langs, N. Paragios

{"title":"Shape priors and discrete MRFs for knowledge-based segmentation","authors":"A. Besbes, N. Komodakis, G. Langs, N. Paragios","doi":"10.1109/CVPR.2009.5206649","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206649","url":null,"abstract":"In this paper we introduce a new approach to knowledge-based segmentation. Our method consists of a novel representation to model shape variations as well as an efficient inference procedure to fit the model to new data. The considered shape model is similarity-invariant and refers to an incomplete graph that consists of intra and intercluster connections representing the inter-dependencies of control points. The clusters are determined according to the co-dependencies of the deformations of the control points within the training set. The connections between the components of a cluster represent the local structure while the connections between the clusters account for the global structure. The distributions of the normalized distances between the connected control points encode the prior model. During search, this model is used together with a discrete Markov random field (MRF) based segmentation, where the unknown variables are the positions of the control points in the image domain. To encode the image support, a Voronoi decomposition of the domain is considered and regional based statistics are used. The resulting model is computationally efficient, can encode complex statistical models of shape variations and benefits from the image support of the entire spatial domain.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128068743","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}

引用次数: 56

Spatiotemporal stereo via spatiotemporal quadric element (stequel) matching 通过时空二次元(stequel)匹配实现时空立体

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206728

Mikhail Sizintsev, Richard P. Wildes

引用次数: 30

Image registration by minimization of residual complexity 残差复杂度最小化的图像配准方法

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206571

A. Myronenko, Xubo B. Song

引用次数: 61

Fast human detection in crowded scenes by contour integration and local shape estimation 基于轮廓积分和局部形状估计的拥挤场景快速人体检测

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206564

Csaba Beleznai, H. Bischof

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引用次数: 61