International Conference on Biometrics Engineering and Application最新文献

Face Liveness Detection by rPPG Features and Contextual Patch-Based CNN 基于rPPG特征和上下文patch的CNN的人脸活动性检测

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345345

Bofan Lin, Xiaobai Li, Zitong Yu, Guoying Zhao

引用次数: 66

SmartHandle: A Novel Behavioral Biometric-based Authentication Scheme for Smart Lock Systems 一种基于行为生物特征的智能锁系统认证方案

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345344

Sandeep Gupta, Attaullah Buriro, B. Crispo

{"title":"SmartHandle: A Novel Behavioral Biometric-based Authentication Scheme for Smart Lock Systems","authors":"Sandeep Gupta, Attaullah Buriro, B. Crispo","doi":"10.1145/3345336.3345344","DOIUrl":"https://doi.org/10.1145/3345336.3345344","url":null,"abstract":"Over recent years, smart locks have evolved as cyber-physical devices that can be operated by digital keypads, physiological biometrics sensors, smart-card readers, or mobile devices pairing, to secure door access. However, the underlying authentication schemes, i.e., knowledge-based (e.g., PIN/passwords), possession-based (e.g., smartphones, smart cards), or physiological biometric-based (e.g., fingerprint, face), utilized in smart locks, have shown several drawbacks. Studies have determined that these authentication schemes are vulnerable to various attacks as well as lack usability. This paper presents SmartHandle - a novel behavioral biometric-based transparent user authentication scheme for smart locks that exploits users' hand-movement while they rotate the door handle to unlock the door. More specifically, our solution models the user's hand-movement in 3-dimensional space by fetching the X, Y, and Z coordinates from 3 sensors, namely, accelerometer, magnetometer, and gyroscope corresponding to the hand-movement trajectory, to generate a user-identification-signature. We validated our solution for a multi-class classification scenario and achieve a True Acceptance Rate (TAR) of 87.27% at the False Acceptance Rate (FAR) of 1.39% with the Linear Discriminant Classifier (LDC) on our collected dataset from 11 users. The solution can be easily deployed at the main entrance of homes and offices offering a secure and usable authentication scheme to their legitimate users.","PeriodicalId":262849,"journal":{"name":"International Conference on Biometrics Engineering and Application","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116749418","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: 7

Graph Embedding for Offline Handwritten Signature Verification 离线手写签名验证的图嵌入

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345346

Michael Stauffer, Paul Maergner, Andreas Fischer, Kaspar Riesen

{"title":"Graph Embedding for Offline Handwritten Signature Verification","authors":"Michael Stauffer, Paul Maergner, Andreas Fischer, Kaspar Riesen","doi":"10.1145/3345336.3345346","DOIUrl":"https://doi.org/10.1145/3345336.3345346","url":null,"abstract":"Due to the high availability and applicability, handwritten signatures are an eminent biometric authentication measure in our life. To mitigate the risk of a potential misuse, automatic signature verification tries to distinguish between genuine and forged signatures. Most of the available signature verification approaches make use of vectorial rather than graph-based representations of the handwriting. This is rather surprising as graphs offer some inherent advantages. Graphs are, for instance, able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. Moreover, several fast graph matching algorithms have been proposed recently that allow to employ graphs also in domains with large amounts of data. The present paper proposes to use different graph embedding approaches in conjunction with a recent graph-based signature verification framework. That is, signature graphs are not directly matched with each other, but first compared with a set of predefined prototype graphs, in order to obtain a dissimilarity representation. In an experimental evaluation, we employ the proposed method on two widely used benchmark datasets. On both datasets, we empirically confirm that the learning-free graph embedding outperforms state-of-the-art methods with respect to both accuracy and runtime.","PeriodicalId":262849,"journal":{"name":"International Conference on Biometrics Engineering and Application","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115738556","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: 3

Detecting Liars in Chats using Keystroke Dynamics 在聊天中使用击键动力学检测说谎者

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345337

Parisa Rezaee Borj, Patrick A. H. Bours

引用次数: 2

Cross-database Micro-Expression Recognition with Deep Convolutional Networks 基于深度卷积网络的跨数据库微表情识别

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345343

Zhaoqiang Xia, Huan Liang, Xiaopeng Hong, Xiaoyi Feng

引用次数: 10

Surface Normals Based Landmarking for 3D Face Recognition Using Photometric Stereo Captures 基于表面法线标记的三维人脸识别方法

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345339

Jiangning Gao, M. Hansen, Melvyn L. Smith, A. Evans

引用次数: 0

Analysis of Factors on BVP Signal Extraction Based on Imaging Principle 基于成像原理的BVP信号提取影响因素分析

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345342

Xiaobiao Zhang, Xiaoyi Feng, Zhaoqiang Xia

{"title":"Analysis of Factors on BVP Signal Extraction Based on Imaging Principle","authors":"Xiaobiao Zhang, Xiaoyi Feng, Zhaoqiang Xia","doi":"10.1145/3345336.3345342","DOIUrl":"https://doi.org/10.1145/3345336.3345342","url":null,"abstract":"The extraction of human physiological based on face video has become a hot research direction, but few researchers pay attention to the process of signal extraction from an optical perspective. This paper establishes an optical model for human skin, and analyzes the principle of extracting human physiological signals from face video by imaging. Based on this model, the effects of melanin and hemoglobin on BVP (blood volume pulse) signal extraction were analyzed. In addition, this paper introduces the model of camera imaging, and discusses the cause of noise generation, analysis of the impact of two kinds of noise on video quality. Finally, this paper uses the MAHNOB database to carry out the heart rate extraction experiment from video, the experiment concluded that: (1) The face with lighter skin color is more conducive to human heart rate extraction; (2) Reducing the signal-noise ratio through compressing the video quality, the impact on the heart rate estimation error reaches minimum as the signal-noise ratio is 30dB; (3) By compressing the original video resolution, color information in the video is reduced, which has a impact on BVP signals extraction, the information recovery can be performed by the method of super-resolution reconstruction.","PeriodicalId":262849,"journal":{"name":"International Conference on Biometrics Engineering and Application","volume":"23 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121106427","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: 3

Subjective Versus Objective Face Image Quality Evaluation For Face Recognition 主观与客观的人脸图像质量评价用于人脸识别

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345338

Ali Khodabakhsh, Marius Pedersen, C. Busch

引用次数: 8

Finger Vein Image Compression with Uniform Background 均匀背景下手指静脉图像压缩

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345347

Babak Maser, Jutta Hämmerle-Uhl, T. Lipowski, A. Uhl

引用次数: 0

Learning of Brain Connectivity Features for EEG-based Person Identification 基于脑电图的人识别的脑连接特征学习

International Conference on Biometrics Engineering and Application Pub Date : 2019-05-29 DOI: 10.1145/3345336.3345340

N. Nyah, Nikolaos Christou

{"title":"Learning of Brain Connectivity Features for EEG-based Person Identification","authors":"N. Nyah, Nikolaos Christou","doi":"10.1145/3345336.3345340","DOIUrl":"https://doi.org/10.1145/3345336.3345340","url":null,"abstract":"The brain activity observed on multiple EEG electrodes is influenced by volume conductance and functional connectivity of a person performing a task. When the task is a biometric test, EEG signals represent the unique 'brain print' which is genetically defined by the functional connectivity that is represented by interactions between the electrodes, whilst the conductance component causes trivial correlations in EEG signals. Orthogonalisation using autoregressive modelling minimises the conductance component, and the connectivity features can be then extracted from the residuals. However, the results cannot be reliable for high-dimensional EEG data recorded via a multi-electrode system. The proposed method shows that the dimensionality can be significantly reduced if baselines that are required for estimating the residuals can be modelled by using EEG electrodes that make important contribution to the functional connectivity. The results show that the required models can be learnt by Machine Learning techniques which are capable of providing the maximal performance in the case of multidimensional EEG data. The study which has been conducted on a EEG benchmark including 109 participants shows a significant improvement of the identification accuracy.","PeriodicalId":262849,"journal":{"name":"International Conference on Biometrics Engineering and Application","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117052417","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: 0