2020 8th International Workshop on Biometrics and Forensics (IWBF)最新文献

Vulnerability Assessment and Detection of Makeup Presentation Attacks 化妆呈现攻击的脆弱性评估与检测

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/IWBF49977.2020.9107961

C. Rathgeb, P. Drozdowski, Daniel Fischer, C. Busch

{"title":"Vulnerability Assessment and Detection of Makeup Presentation Attacks","authors":"C. Rathgeb, P. Drozdowski, Daniel Fischer, C. Busch","doi":"10.1109/IWBF49977.2020.9107961","DOIUrl":"https://doi.org/10.1109/IWBF49977.2020.9107961","url":null,"abstract":"The accuracy of face recognition systems can be negatively affected by facial cosmetics which have the ability to substantially alter the facial appearance. Recently, it was shown that makeup can also be abused to launch so-called makeup presentation attacks. In such attacks, an attacker might apply heavy makeup to achieve the facial appearance of a target subject for the purpose of impersonation.In this work, we assess the vulnerability of a widely used open-source face recognition system, i.e. ArcFace, to makeup presentation attacks using the publicly available Makeup Induced Face Spoofing (MIFS) and FRGCv2 databases. It is shown that the success rate of makeup presentation attacks in the MIFS database has negligible impact on the security of the face recognition system. Further, we employ image warping to simulate improved makeup presentation attacks which reveal a significantly higher success rate. Moreover, we propose a makeup attack detection scheme which compares face depth data with face depth reconstructions obtained from RGB images of potential makeup presentation attacks. Significant variations between the two sources of information indicate facial shape alterations induced by strong use of makeup, i.e. potential makeup presentation attacks. Conceptual experiments on the MIFS database confirm the soundness of the presented approach.","PeriodicalId":174654,"journal":{"name":"2020 8th International Workshop on Biometrics and Forensics (IWBF)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114787652","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: 10

Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition 头戴式显示器的眼周生物识别:一种更好识别的样本选择方法

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/IWBF49977.2020.9107939

F. Boutros, N. Damer, K. Raja, Raghavendra Ramachandra, Florian Kirchbuchner, Arjan Kuijper

{"title":"Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition","authors":"F. Boutros, N. Damer, K. Raja, Raghavendra Ramachandra, Florian Kirchbuchner, Arjan Kuijper","doi":"10.1109/IWBF49977.2020.9107939","DOIUrl":"https://doi.org/10.1109/IWBF49977.2020.9107939","url":null,"abstract":"Virtual and augmented reality technologies are increasingly used in a wide range of applications. Such technologies employ a Head Mounted Display (HMD) that typically includes an eye-facing camera and is used for eye tracking. As some of these applications require accessing or transmitting highly sensitive private information, a trusted verification of the operator’s identity is needed. We investigate the use of HMD-setup to perform verification of operator using periocular region captured from inbuilt camera. However, the uncontrolled nature of the periocular capture within the HMD results in images with a high variation in relative eye location and eye-opening due to varied interactions. Therefore, we propose a new normalization scheme to align the ocular images and then, a new reference sample selection protocol to achieve higher verification accuracy. The applicability of our proposed scheme is exemplified using two handcrafted feature extraction methods and two deep-learning strategies. We conclude by stating the feasibility of such a verification approach despite the uncontrolled nature of the captured ocular images, especially when proper alignment and sample selection strategy is employed.","PeriodicalId":174654,"journal":{"name":"2020 8th International Workshop on Biometrics and Forensics (IWBF)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114993690","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

IWBF 2020 Cover Page IWBF 2020封面

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/iwbf49977.2020.9107941

引用次数: 0

IWBF 2020 Blank Page IWBF 2020空白页

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/iwbf49977.2020.9107959

引用次数: 0

Secure Triplet Loss for End-to-End Deep Biometrics 端到端深度生物识别的安全三重丢失

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/IWBF49977.2020.9107958

João Ribeiro Pinto, Jaime S. Cardoso, M. V. Correia

引用次数: 7

Template Protection on Multiple Facial Biometrics in the Signal Domain under Visible and Near-Infrared Light 可见光和近红外光下信号域多重面部生物特征的模板保护

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/IWBF49977.2020.9107964

Simon Kirchgasser, L. Debiasi, R. Schraml, H. Hofbauer, A. Uhl, Jonathan N. Boyle, J. Ferryman

引用次数: 2

IWBF 2020 Table of Contents IWBF 2020目录

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/iwbf49977.2020.9107965

引用次数: 0

Spatial-Temporal Omni-Scale Feature Learning for Person Re-Identification 时空全尺度特征学习对人物再识别的影响

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/IWBF49977.2020.9107966

Aida Pločo, Andrea Macarulla Rodriguez, Z. Geradts

{"title":"Spatial-Temporal Omni-Scale Feature Learning for Person Re-Identification","authors":"Aida Pločo, Andrea Macarulla Rodriguez, Z. Geradts","doi":"10.1109/IWBF49977.2020.9107966","DOIUrl":"https://doi.org/10.1109/IWBF49977.2020.9107966","url":null,"abstract":"State-of-the-art person re-identification (ReID) models use Convolutional Neural Networks (CNN) for feature extraction and comparison. Often these models fail to recognize all the intra- and inter-class variations that emerge in person ReID, making it harder to discriminate between data subjects. In this paper we seek to reduce these problems and improve performance by combining two state-of-the-art models. We use the Omni-Scale Network (OSNet) as our CNN to test the Market1501 and DukeMTMC-ReID datasets for person ReID. To fully utilize the potential of these datasets, we apply the spatialtemporal constraint which extracts the camera ID and timestamp from each image to form a distribution. We combine these two methods to create a hybrid model titled Spatial-Temporal OmniScale Network (st-OSNet). Our model attains a Rank-1 (R1) accuracy of 98.2% and mean average precision (mAP) of 92.7% for the Market1501 dataset. For the DukeMTMC-reID dataset our model achieves 94.3% R1 and 86.1% mAP, hereby surpassing the results of OSNet by a large margin for both datasets (94.3%, 86.4%, 88.4%, 76.1%, respectively).","PeriodicalId":174654,"journal":{"name":"2020 8th International Workshop on Biometrics and Forensics (IWBF)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116770660","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

Comparison-Level Mitigation of Ethnic Bias in Face Recognition 人脸识别中种族偏见的比较缓解

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/IWBF49977.2020.9107956

Philipp Terhörst, M. Tran, N. Damer, Florian Kirchbuchner, Arjan Kuijper

{"title":"Comparison-Level Mitigation of Ethnic Bias in Face Recognition","authors":"Philipp Terhörst, M. Tran, N. Damer, Florian Kirchbuchner, Arjan Kuijper","doi":"10.1109/IWBF49977.2020.9107956","DOIUrl":"https://doi.org/10.1109/IWBF49977.2020.9107956","url":null,"abstract":"Current face recognition systems achieve high performance on several benchmark tests. Despite this progress, recent works showed that these systems are strongly biased against demographic sub-groups. Previous works introduced approaches that aim at learning less biased representations. However, applying these approaches in real applications requires a complete replacement of the templates in the database. This replacement procedure further requires that a face image of each enrolled individual is stored as well. In this work, we propose the first bias-mitigating solution that works on the comparison-level of a biometric system. We propose a fairness- driven neural network classifier for the comparison of two biometric templates to replace the systems similarity function. This fair classifier is trained with a novel penalization term in the loss function to introduce the criteria of group and individual fairness to the decision process. This penalization term forces the score distributions of different ethnicities to be similar, leading to a reduction of the intra-ethnic performance differences. Experiments were conducted on two publicly available datasets and evaluated the performance of four different ethnicities. The results showed that for both fairness criteria, our proposed approach is able to significantly reduce the ethnic bias, while it preserves a high recognition ability. Our model, build on individual fairness, achieves bias reduction rate between 15.35% and 52.67%. In contrast to previous work, our solution is easy to integrate into existing systems by simply replacing the systems similarity functions with our fair template comparison approach.","PeriodicalId":174654,"journal":{"name":"2020 8th International Workshop on Biometrics and Forensics (IWBF)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124260382","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: 23

Deep Learning Based Stress Prediction From Offline Signatures 基于离线签名的深度学习压力预测

2020 8th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2020-04-01 DOI: 10.1109/IWBF49977.2020.9107942

Hakan Yekta Yatbaz, Meryem Erbilek

引用次数: 1