2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)最新文献_第2页

Faking it, Making it: Fooling and Improving Brain-Based Authentication with Generative Adversarial Networks 假装它，制造它:用生成对抗网络愚弄和改进基于大脑的认证

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698606

Tanya Piplani, Nick Merrill, J. Chuang

引用次数: 13

On the Use of Mobile Calling Patterns for Soft Biometric Classification 手机通话模式在软生物识别分类中的应用研究

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698591

T. Neal, D. Woodard

引用次数: 2

On Efficiency and Effectiveness Tradeoffs in High-Throughput Facial Biometric Recognition Systems 高通量面部生物识别系统的效率与有效性权衡

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698598

John J. Howard, Andrew J. Blanchard, Yevgeniy B. Sirotin, Jacob A. Hasselgren, A. Vemury

{"title":"On Efficiency and Effectiveness Tradeoffs in High-Throughput Facial Biometric Recognition Systems","authors":"John J. Howard, Andrew J. Blanchard, Yevgeniy B. Sirotin, Jacob A. Hasselgren, A. Vemury","doi":"10.1109/BTAS.2018.8698598","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698598","url":null,"abstract":"This research discusses the evaluation of biometric systems that are designed to process hundreds to tens of thousands of individuals in short time spans. We propose a method for evaluating a system's performance across capture attempts for the purpose of identifying characteristics that are advantageous in these high-throughput environments. We also present a novel modification to the traditionally accepted biometric performance metrics of failure-to-acquire, and true-match rate. Namely, this paradigm shift holds that these metrics are a function of time and, as such, vary with the time available for a biometric system to interact with a user. This research demonstrates the utility of these time-based metrics in evaluating the performance of multiple, commercially available, high-throughput systems. We show that different biometric systems have notably different time-based performance curves using a corpus of data collected during the 2018 Department of Homeland Security, Science and Technology Directorate (DHS S&T) Biometric Technology Rally. These curves and the deviations between them are useful when quantifying the suitability of a technology, evaluated via scenario testing, for deployment in an operational environment where the throughput of the target population is a key performance parameter.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133410701","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

Mind the Gap: A Practical Framework for Classifiers in a Forensic Context 注意差距:法医学语境中分类器的实用框架

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698583

C. Zeinstra, D. Meuwly, R. Veldhuis, L. Spreeuwers

引用次数: 4

Orientation Field Estimation for Latent Fingerprints by Exhaustive Search of Large Database 基于大数据库穷举搜索的潜在指纹方向场估计

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698594

Qihao Yin, Jianjiang Feng, Jiwen Lu, Jie Zhou

{"title":"Orientation Field Estimation for Latent Fingerprints by Exhaustive Search of Large Database","authors":"Qihao Yin, Jianjiang Feng, Jiwen Lu, Jie Zhou","doi":"10.1109/BTAS.2018.8698594","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698594","url":null,"abstract":"Orientation field estimation is one of the most important steps in latent fingerprint recognition systems. However, due to very poor image quality, the performances of the state-of-the-art algorithms of orientation field estimation are still far from satisfactory. Based on the assumption that a sufficiently large fingerprint database should contain fingerprints whose orientation fields are quite similar to a latent fingerprint, we propose an orientation field estimation algorithm based on exhaustive search of large database for nearest neighbor. We set up a large database of 10,000 fingerprints of good quality, whose orientation fields and poses are estimated offline using traditional methods. Given a latent fingerprint as input, the most similar orientation field in the database is found and is combined with the original latent image to obtain the final orientation field. As a by product, we also obtain the pose of the latent fingerprint, which can be useful for fingerprint registration. The experimental results on NIST SD27 latent database show our method performs better than the state-of-the-art algorithms, in both orientation field estimation accuracy and identification performance, especially on those fingerprints of very poor quality.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114947361","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

Biometric Distinctiveness of Brain Signals Based on EEG 基于脑电图的脑信号生物特征特征分析

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698540

Sukun Li, Sung-Hyuk Cha, C. Tappert

引用次数: 7

Dual-Minimax Probability Machines for One-class Mobile Active Authentication 一类移动主动认证的双极大极小概率机

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698603

Pramuditha Perera, Vishal M. Patel

引用次数: 18

BTAS 2018 Program BTAS 2018计划

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/btas.2018.8698592

引用次数: 0

BTAS 2018 Organizing Committee BTAS 2018组委会

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/btas.2018.8698582

引用次数: 0

An Investigation of High-Throughput Biometric Systems: Results of the 2018 Department of Homeland Security Biometric Technology Rally 高通量生物识别系统的调查:2018年国土安全部生物识别技术集会的结果

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698547

John J. Howard, Andrew J. Blanchard, Yevgeniy B. Sirotin, Jacob A. Hasselgren, A. Vemury

{"title":"An Investigation of High-Throughput Biometric Systems: Results of the 2018 Department of Homeland Security Biometric Technology Rally","authors":"John J. Howard, Andrew J. Blanchard, Yevgeniy B. Sirotin, Jacob A. Hasselgren, A. Vemury","doi":"10.1109/BTAS.2018.8698547","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698547","url":null,"abstract":"The 2018 Biometric Technology Rally was an evaluation, sponsored by the U.S. Department of Homeland Security, Science and Technology Directorate (DHS S&T), that challenged industry to provide face or face/iris systems capable of unmanned, traveler identification in a high-throughput security environment. Selected systems were installed at the Maryland Test Facility (MdTF), a DHS S&T affiliated bio-metrics testing laboratory, and evaluated using a population of 363 naive human subjects recruited from the general public. The performance of each system was examined based on measured throughput, capture capability, matching capability, and user satisfaction metrics. This research documents the performance of unmanned face and face/iris systems required to maintain an average total subject interaction time of less than 10 seconds. The results highlight discrepancies between the performance of biometric systems as anticipated by the system designers and the measured performance, indicating an incomplete understanding of the main determinants of system performance. Our research shows that failure-to-acquire errors, unpredicted by system designers, were the main driver of non-identification rates instead of failure-to-match errors, which were better predicted. This outcome indicates the need for a renewed focus on reducing the failure-to-acquire rate in high-throughput, unmanned biometric systems.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"21 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131957281","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