2018 International Conference on Biometrics (ICB)最新文献

Conformal Mapping of a 3D Face Representation onto a 2D Image for CNN Based Face Recognition 基于CNN的人脸识别中3D人脸表示到2D图像的保角映射

2018 International Conference on Biometrics (ICB) Pub Date : 2018-07-16 DOI: 10.1109/ICB2018.2018.00029

J. Kittler, P. Koppen, P. Kopp, P. Huber, Matthias Rätsch

引用次数: 17

Multifactor User Authentication with In-Air-Handwriting and Hand Geometry 多因素用户认证与空中手写和手几何

2018 International Conference on Biometrics (ICB) Pub Date : 2018-07-13 DOI: 10.1109/ICB2018.2018.00046

Duo Lu, Dijiang Huang, Yuli Deng, Adel Alshamrani

{"title":"Multifactor User Authentication with In-Air-Handwriting and Hand Geometry","authors":"Duo Lu, Dijiang Huang, Yuli Deng, Adel Alshamrani","doi":"10.1109/ICB2018.2018.00046","DOIUrl":"https://doi.org/10.1109/ICB2018.2018.00046","url":null,"abstract":"On wearable and Virtual Reality (VR) platforms, user authentication is a basic function, but usually a keyboard or touchscreen cannot be provided to type a password. Hand gesture and especially in-air-handwriting can be potentially used for user authentication because a gesture input interface is readily available on these platforms. However, determining whether a login request is from the legitimate user based on a piece of hand movement is challenging in both signal processing and matching, which leads to limited performance in existing systems. In this paper, we propose a multifactor user authentication framework using both the motion signal of a piece of in-air-handwriting and the geometry of hand skeleton captured by a depth camera. To demonstrate this framework, we invented a signal matching algorithm, implemented a prototype, and conducted experiments on a dataset of 100 users collected by us. Our system achieves 0.6% Equal Error Rate (EER) without spoofing attack and 3.4% EER with spoofing only data, which is a significant improvement compared to existing systems using the Dynamic Time Warping (DTW) algorithm. In addition, we presented an in-depth analysis of the utilized features to explain the reason for the performance boost.","PeriodicalId":130957,"journal":{"name":"2018 International Conference on Biometrics (ICB)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128587051","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: 17

SSBC 2018: Sclera Segmentation Benchmarking Competition SSBC 2018:巩膜分割标杆竞赛

2018 International Conference on Biometrics (ICB) Pub Date : 2018-07-13 DOI: 10.1109/BTAS.2015.7358796

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

{"title":"SSBC 2018: Sclera Segmentation Benchmarking Competition","authors":"Abhijit Das, U. Pal, M. A. Ferrer-Ballester, M. Blumenstein","doi":"10.1109/BTAS.2015.7358796","DOIUrl":"https://doi.org/10.1109/BTAS.2015.7358796","url":null,"abstract":"This paper summarises the results of the Sclera Segmentation Benchmarking Competition (SSBC 2018). It was organised in the context of the 11th IAPR International Conference on Biometrics (ICB 2018). The aim of this competition was to record the developments on sclera segmentation in the cross-sensor environment (sclera trait captured using multiple acquiring sensors). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have developed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1), which was used in the context of the previous versions of sclera segmentation competitions. The images in the second dataset were captured using .a mobile phone rear camera of 8-megapixel. As baseline manual segmentation mask of the sclera images from both the datasets were developed. Precision and recall-based statistical measures were employed to evaluate the effectiveness of the submitted segmentation technique and to rank them. Six algorithms were submitted towards the segmentation task. This paper analyses the results produced by these algorithms/system and defines a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be freely available for research purposes upon request to authors by email.","PeriodicalId":130957,"journal":{"name":"2018 International Conference on Biometrics (ICB)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128353162","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: 24

Evolutionary Methods for Generating Synthetic MasterPrint Templates: Dictionary Attack in Fingerprint Recognition 合成主指纹模板的进化生成方法:指纹识别中的字典攻击

2018 International Conference on Biometrics (ICB) Pub Date : 2018-07-13 DOI: 10.1109/ICB2018.2018.00017

Aditi Roy, N. Memon, J. Togelius, A. Ross

引用次数: 21

Two-Stream Part-Based Deep Representation for Human Attribute Recognition 基于双流部分的人类属性识别深度表示

2018 International Conference on Biometrics (ICB) Pub Date : 2018-07-13 DOI: 10.1109/ICB2018.2018.00024

R. Anwer, F. Khan, Jorma T. Laaksonen

{"title":"Two-Stream Part-Based Deep Representation for Human Attribute Recognition","authors":"R. Anwer, F. Khan, Jorma T. Laaksonen","doi":"10.1109/ICB2018.2018.00024","DOIUrl":"https://doi.org/10.1109/ICB2018.2018.00024","url":null,"abstract":"Recognizing human attributes in unconstrained environments is a challenging computer vision problem. State-of-the-art approaches to human attribute recognition are based on convolutional neural networks (CNNs). The de facto practice when training these CNNs on a large labeled image dataset is to take RGB pixel values of an image as input to the network. In this work, we propose a two-stream part-based deep representation for human attribute classification. Besides the standard RGB stream, we train a deep network by using mapped coded images with explicit texture information, that complements the standard RGB deep model. To integrate human body parts knowledge, we employ the deformable part-based models together with our two-stream deep model. Experiments are performed on the challenging Human Attributes (HAT-27) Dataset consisting of 27 different human attributes. Our results clearly show that (a) the two-stream deep network provides consistent gain in performance over the standard RGB model and (b) that the attribute classification results are further improved with our two-stream part-based deep representations, leading to state-of-the-art results.","PeriodicalId":130957,"journal":{"name":"2018 International Conference on Biometrics (ICB)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939241","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: 1

LivDet 2017 Fingerprint Liveness Detection Competition 2017 LivDet 2017指纹活性检测大赛

2018 International Conference on Biometrics (ICB) Pub Date : 2018-03-14 DOI: 10.1109/ICB2018.2018.00052

V. Mura, G. Orrú, Roberto Casula, A. Sibiriu, G. Loi, Pierluigi Tuveri, Luca Ghiani, G. Marcialis

引用次数: 56

Comparative Study of Digital Fingerprint Quality Assessment Metrics 数字指纹质量评价指标的比较研究

2018 International Conference on Biometrics (ICB) Pub Date : 2018-02-20 DOI: 10.1109/ICB2018.2018.00014

Zhigang Yao, J. L. Bars, C. Charrier, C. Rosenberger

引用次数: 3

Boosting Face in Video Recognition via CNN Based Key Frame Extraction 基于CNN的关键帧提取增强视频识别中的人脸

2018 International Conference on Biometrics (ICB) Pub Date : 2018-02-01 DOI: 10.1109/ICB2018.2018.00030

Xuan Qi, Chen Liu, S. Schuckers

{"title":"Boosting Face in Video Recognition via CNN Based Key Frame Extraction","authors":"Xuan Qi, Chen Liu, S. Schuckers","doi":"10.1109/ICB2018.2018.00030","DOIUrl":"https://doi.org/10.1109/ICB2018.2018.00030","url":null,"abstract":"Face in video recognition (FiVR) technology is widely applied in various fields such as video analytics and real-time video surveillance. However, FiVR technology also faces the challenges of high-volume video data, real-time processing requirement, as well as improving the performance of face recognition (FR) algorithms. To overcome these challenges, frame selection becomes a necessary and beneficial step before the FR stage. In this paper, we propose a CNN-based key-frame extraction (KFE) engine with GPU acceleration, employing our innovative Face Quality Assessment (FQA) module. For theoretical performance analysis of the KFE engine, we evaluated representative one-person video datasets such as PaSC, FiA and ChokePoint using ROC and DET curves. For performance analysis under practical scenario, we evaluated multi-person videos using ChokePoint dataset as well as in-house captured full-HD videos. The experimental results show that our KFE engine can dramatically reduce the data volume while improving the FR performance. In addition, our KFE engine can achieve higher than real-time performance with GPU acceleration in dealing with HD videos in real application scenarios.","PeriodicalId":130957,"journal":{"name":"2018 International Conference on Biometrics (ICB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127547313","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: 18

Securing Minutia Cylinder Codes for Fingerprints through Physically Unclonable Functions: An Exploratory Study 通过物理不可克隆功能保护指纹的微小圆柱体代码:一项探索性研究

2018 International Conference on Biometrics (ICB) Pub Date : 2018-02-01 DOI: 10.1109/ICB2018.2018.00019

Rosario Arjona, Miguel A. Prada-Delgado, I. Baturone, A. Ross

引用次数: 13

Improving 2D Face Recognition via Discriminative Face Depth Estimation 基于判别性人脸深度估计的二维人脸识别改进

2018 International Conference on Biometrics (ICB) Pub Date : 2018-02-01 DOI: 10.1109/ICB2018.2018.00031

Jiyun Cui, Hao Zhang, Hu Han, S. Shan, Xilin Chen

引用次数: 47