2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications最新文献

Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation 利用人工神经网络和特征显著性来识别包含最具歧视性信息的虹膜测量值，用于虹膜分割

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925685

R. Broussard, R. Ives

{"title":"Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation","authors":"R. Broussard, R. Ives","doi":"10.1109/CIB.2009.4925685","DOIUrl":"https://doi.org/10.1109/CIB.2009.4925685","url":null,"abstract":"One of the basic challenges to robust iris recognition is iris segmentation. To represent the iris, some researchers fit circles, ellipses or active contours to the boundary pixels of the segmented iris. In order to get an accurate fit, the iris boundary must first be accurately identified. Some segmentation methods operate on a pre-processed gray-scaled image, while others use a thresholded binary edge image. The Hough transform is a popular method used to search for circular or elliptical patterns within the image. Many irises are slightly elliptical, and suffer from eyelid/eyelash occlusion, specular reflections and often the pupil and iris centers are not co-located. Each of these issues can cause a segmentation error. This research uses of a feature saliency algorithm to identify which measurements, used in common iris segmentation methods, jointly contain the most discriminatory information for identify the iris boundary. Once this feature set is identified, an artificial neural network is used to near-optimally combine the segmentation measurements to better localize and identify boundary pixels of the iris. In this approach, no assumption of circularity is assumed when identifying the iris boundary. 322 measurements were tested and eight were found to contain discriminatory information that can assist in identifying the iris boundary. For occluded images, the iris masks created by the neural network were consistently more accurate than the truth mask created using the circular iris boundary assumption.","PeriodicalId":395538,"journal":{"name":"2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129052347","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: 20

Multi-level iris video image thresholding 多级虹膜视频图像阈值分割

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925684

Yingzi Du, Senior Member, N. L. Thomas, Emrah Arslanturk

{"title":"Multi-level iris video image thresholding","authors":"Yingzi Du, Senior Member, N. L. Thomas, Emrah Arslanturk","doi":"10.1109/CIB.2009.4925684","DOIUrl":"https://doi.org/10.1109/CIB.2009.4925684","url":null,"abstract":"Iris recognition has been shown to be one of the most accurate biometrics. However, under non-ideal situations, its recognition accuracy can be reduced dramatically. Under such situations, video images can be used to improve the accuracy. The traditional single image based segmentation method could be inefficient. Video image based thresholding method can help improve the segmentation efficiency. However, traditional thresholding methods are not designed for iris images. In this paper, the multi-level iris video image thresholding method is proposed. It takes advantage of the correlations between consecutive images for video based thresholding. It is an orientation invariant thresholding scheme. K-mean clustering is used to find the clusters and PCA is used to quickly project the image to the clusters. The experimental results show the proposed method is effective. The thresholded images show clear pupil and iris areas, which can help further iris segmentation and processing. In addition, the proposed method can be applied to non-video images if they are obtained from the same sensor with similar illumination conditions.","PeriodicalId":395538,"journal":{"name":"2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications","volume":"461 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122804467","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: 8

Artificial neural network based automatic face parts prediction system from only fingerprints 基于人工神经网络的人脸指纹自动预测系统

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925690

Ş. Sağiroğlu, Necla Özkaya

{"title":"Artificial neural network based automatic face parts prediction system from only fingerprints","authors":"Ş. Sağiroğlu, Necla Özkaya","doi":"10.1109/CIB.2009.4925690","DOIUrl":"https://doi.org/10.1109/CIB.2009.4925690","url":null,"abstract":"Biometrics is a deeply studied and highly developed technology. While biometric systems have been used primarily in limited applications requiring high security tasks like criminal identification and police work, more recently they have been receiving increasing demand for person recognition applications. In spite of all developments in biometrics, there is no study investigating relationships between biometric features in the literature. This study presents a novel intelligent approach analysing the existence of any relationship among fingerprints and face parts. Proposed approach is based on artificial neural networks. Developed system generates the stationary face parts of a person including eyebrows, eyes and nose from only one fingerprint image of the same person without knowing any information about his or her face with the errors among 1.4 % and 4.8 %. The satisfactory results have indicated that there are close realitionships among fingerprints and faces. Improving of the proposed system is still sustained for the purpose of analysing and modelling of this relationship for the future developments in biometrics and security applications.","PeriodicalId":395538,"journal":{"name":"2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126094268","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: 11

Minimizing the number of bits needed for iris recognition via Bit Inconsistency and GRIT 通过位不一致性和GRIT最小化虹膜识别所需的位数

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925683

G. Dozier, Kurt Frederiksen, Robert Meeks, M. Savvides, Kelvin S. Bryant, Darlene Hopes, T. Munemoto

引用次数: 36

Automatic iris mask refinement for high performance iris recognition 自动虹膜掩膜细化高性能虹膜识别

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925686

Yung-Hui Li, M. Savvides

引用次数: 3

Effects of image compression on iris recognition performance and image quality 图像压缩对虹膜识别性能和图像质量的影响

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925681

R. Ives, Daniel A. Bishop, Yingzi Du, C. Belcher

引用次数: 8

A facial presence monitoring system for information security 一种用于信息安全的面部存在监测系统

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925689

Q. Xiao, Xue-Dong Yang

{"title":"A facial presence monitoring system for information security","authors":"Q. Xiao, Xue-Dong Yang","doi":"10.1109/CIB.2009.4925689","DOIUrl":"https://doi.org/10.1109/CIB.2009.4925689","url":null,"abstract":"Information security requires a method to establish digital credentials that can reliably identify individual users. Since biometrics is concerned with the measurements of unique human physiological or behavioural characteristics, the technology has been used to verify the identity of computer or network users. Given today's heightened security requirements of military as well as other applications such as banking, health care, etc., it is becoming critical to be able to monitor the presence of the authenticated user throughout a session. This paper presents a prototype system that uses facial recognition technology to monitor the authenticated user. The objective is to ensure that the user who is using the computer is the same person that logged onto the system. A neural network-based algorithm is implemented to carry out face detection, and an eigenface method is employed to perform facial recognition. A graphical user interface (GUI) has been developed which allows the performance of face detection and facial recognition to be monitored at run time. The experimental results demonstrate the feasibility of near-real-time continuous user verification for high-level security information systems.","PeriodicalId":395538,"journal":{"name":"2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129142368","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: 21

An efficient dual stage approach for iris feature extraction using interest point pairing 基于兴趣点配对的虹膜特征提取方法

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925687

H. Mehrotra, G. Badrinath, B. Majhi, Phalguni Gupta

引用次数: 13

Inverse operation and preimage attack on BioHashing 生物哈希的逆操作和预像攻击

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925692

Yongjin Lee, Yun-Su Chung, Kiyoung Moon

引用次数: 39

Transforming traditional iris recognition systems to work on non-ideal situations 改造传统的虹膜识别系统，使其在非理想情况下工作

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI: 10.1109/CIB.2009.4925679

Zhi Zhou, Yingzi Du, C. Belcher

{"title":"Transforming traditional iris recognition systems to work on non-ideal situations","authors":"Zhi Zhou, Yingzi Du, C. Belcher","doi":"10.1109/CIB.2009.4925679","DOIUrl":"https://doi.org/10.1109/CIB.2009.4925679","url":null,"abstract":"Non-ideal iris images can significantly affect the accuracy of iris recognition systems for two reasons: 1) they cannot be properly preprocessed by the system; and/or 2) they have poor image quality. However, many traditional iris recognition systems have been deployed in law enforcement, military, or many other important locations. It will be expensive to replace all these systems. It will be desirable if the traditional systems can be transformed to perform in non-ideal situations without an expensive update. In this paper, we propose a method that can help traditional iris recognition systems to work on the non-ideal situation using a video image approach. The proposed method will quickly identify and eliminate the bad quality images from iris videos for further processing. The segmentation accuracy is critical in recognition and would be challenging for traditional systems. The segmentation evaluation is designed to evaluate if the segmentation is valid. The information distance based quality measure is used to evaluate if the image has enough quality for recognition. The segmentation evaluation score and quality score are combined to predict the recognition performance. The research results show that the proposed methods can work effectively and objectively. The combination of segmentation and quality scores is highly correlated with the recognition accuracy and can be used to improve the performance of iris recognition systems in a non-ideal situation. The deployment of such a system would not cost much since the core parts of the traditional systems are not changed and we only need to add software modules. It will be very practical to transform the traditional system using the proposed method.","PeriodicalId":395538,"journal":{"name":"2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114382865","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: 85