2016 International Conference on Biometrics (ICB)最新文献_第3页

Calculating a boundary for the significance from the equal-error rate 从等错误率计算显著性的边界

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550053

H. Hofbauer, A. Uhl

引用次数: 11

Meandering energy potential to locate singular point of fingerprint 弯曲能量势定位指纹奇点

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550086

Kamlesh Tiwari, Phalguni Gupta

引用次数: 5

Binary search path of vocabulary tree based finger vein image retrieval 基于词汇树的指静脉图像检索二叉搜索路径

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550056

Kuikui Wang, Lu Yang, Kun Su, Gongping Yang, Yilong Yin

引用次数: 8

Fine-grained LFW database 细粒度的LFW数据库

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550057

Nanhai Zhang, Weihong Deng

{"title":"Fine-grained LFW database","authors":"Nanhai Zhang, Weihong Deng","doi":"10.1109/ICB.2016.7550057","DOIUrl":"https://doi.org/10.1109/ICB.2016.7550057","url":null,"abstract":"Current deep learning methods have achieved human-level performance on Labeled Faces in the Wild (LFW) database, but we think it is because that the limited number of pairs on LFW do not capture the real difficulty of large-scale unconstrained face verification problem. Besides the intra-class variations like pose, illumination, occlusion and expression, highly visually similarity of different persons' faces is an another challenge. It is unavoidable in large dataset and many researchers ignore it. Therefore, in this paper, we firstly select some visually similar pairs in LFW database by combining the deep learning method and human annotation results. Preserving the matched pairs and replacing the mismatched pairs of LFW with the selected similar pairs, we obtain the Fine-grained LFW (FGLFW) database which can better reflect the real difficulty of face verification. Experimental results show that methods achieving not bad performance on LFW drops more than 11% even 25% on FGLFW. It reflects that visually similar pairs are difficult to current methods and our FGLFW database is a quite challenging database. Researchers still have a long way to go for solving face verification problem on such a database.","PeriodicalId":308715,"journal":{"name":"2016 International Conference on Biometrics (ICB)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124335958","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

Tattoo detection based on CNN and remarks on the NIST database 基于CNN和NIST数据库注释的纹身检测

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550050

Qingyong Xu, Soham Ghosh, Xingpeng Xu, Yi Huang, A. Kong

{"title":"Tattoo detection based on CNN and remarks on the NIST database","authors":"Qingyong Xu, Soham Ghosh, Xingpeng Xu, Yi Huang, A. Kong","doi":"10.1109/ICB.2016.7550050","DOIUrl":"https://doi.org/10.1109/ICB.2016.7550050","url":null,"abstract":"Detecting tattoo images stored in information technology (IT) devices of suspects is an important but challenging task for law enforcement agencies. Recently, the U.S. National Institute of Standards and Technology (NIST) held a challenge and released a tattoo database for the commercial and academic community in advancing research and development into automated image-based tattoo recognition technology. The best tattoo detection result in the NIST challenge was achieved by MorphoTrak with accuracy of 96.3%. This paper aims to answer three questions. 1) Is the NIST database suitable for training algorithms to detect tattoo images stored in IT devices of suspects? 2) Can convolutional neural networks (CNNs) outperform the MorphoTrak's algorithm? 3) How do training databases impact on tattoo detection performance? The NIST tattoo detection database containing 2,349 images and a database containing 10,000 collected from Flickr are utilized to answer these questions. The Flickr images taken in diverse environments and poses are used to simulate images stored in the IT devices. A CNN is trained on the NIST and Flickr images for this study. The experimental results demonstrate that the CNN outperforms the MorphoTrak's algorithm by 2.5%, achieving accuracy of 98.8% on the NIST database. When the CNN is trained on the NIST database to detect Flickr images, the accuracy drops to 65.8%. It implies that the NIST database is not an ideal database for training algorithms to detect tattoo images in IT devices of suspects. However, when the training database size increases, the detection performance improves.","PeriodicalId":308715,"journal":{"name":"2016 International Conference on Biometrics (ICB)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115564667","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

GEINet: View-invariant gait recognition using a convolutional neural network 基于卷积神经网络的视点不变步态识别

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550060

Kohei Shiraga, Yasushi Makihara, D. Muramatsu, T. Echigo, Y. Yagi

引用次数: 301

Reliable face anti-spoofing using multispectral SWIR imaging 使用多光谱SWIR成像可靠的面部抗欺骗

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550052

Holger Steiner, A. Kolb, Norbert Jung

引用次数: 73

Methodology for partial fingerprint enrollment and authentication on mobile devices 移动设备上的部分指纹登记和认证方法

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550093

S. Mathur, Ankit Vjay, Jidnya Shah, Shreyasi Das, Adil Malla

引用次数: 34

SSRBC 2016: Sclera Segmentation and Recognition Benchmarking Competition SSRBC 2016:巩膜分割与识别标杆竞赛

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550069

Abhijit Das, U. Pal, M. A. Ferrer-Ballester, M. Blumenstein

{"title":"SSRBC 2016: Sclera Segmentation and Recognition Benchmarking Competition","authors":"Abhijit Das, U. Pal, M. A. Ferrer-Ballester, M. Blumenstein","doi":"10.1109/ICB.2016.7550069","DOIUrl":"https://doi.org/10.1109/ICB.2016.7550069","url":null,"abstract":"This article reports and summarizes the results of a competition on sclera segmentation and recognition benchmarking, called Sclera Segmentation and Recognition Benchmarking Competition 2016 (SSRBC 2016). It was organized in the context of the 9th IAPR International Conference on Biometrics (ICB 2016). The goal of this competition was to record the recent developments in sclera segmentation and recognition, and also to gain the attention of researchers on this subject of biometrics. In this regard, we have used a multi-angle sclera dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) different eyes. We have prepared a manual segmentation mask of these images to create the baseline for both tasks. We have, furthermore, adopted precision and recall based statistical measures to evaluate the effectiveness of the segmentation and the ranks of the competing algorithms. The recognition accuracy measure has been employed to measure the recognition task. To summarize, twelve participants registered for the competition, and among them, three participants submitted their algorithms/ systems for the segmentation task and two their recognition algorithm. The results produced by these algorithms reflect developments in the literature of sclera segmentation and recognition, employing cutting edge segmentation techniques. Along with the algorithms of three competing teams and their results, the MASD version 1 dataset will also be freely available for research purposes from the organizer's website. The competition also demonstrates the recent interests of researchers from academia as well as industry on this subject of biometrics.","PeriodicalId":308715,"journal":{"name":"2016 International Conference on Biometrics (ICB)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116974374","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: 35

FVRC2016: The 2nd Finger Vein Recognition Competition FVRC2016:第二届手指静脉识别大赛

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550051

Yapeng Ye, Liao Ni, He Zheng, Shilei Liu, Yi Zhu, Deng Zhang, Weilai Xiang, Wenxin Li

引用次数: 12