2020 IEEE International Joint Conference on Biometrics (IJCB)最新文献_第9页

How Does Gender Balance In Training Data Affect Face Recognition Accuracy? 训练数据中的性别平衡如何影响人脸识别的准确性?

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-02-07 DOI: 10.1109/IJCB48548.2020.9304924

Vítor Albiero, Kai Zhang, K. Bowyer

引用次数: 43

Identity Document to Selfie Face Matching Across Adolescence 青少年身份证件与自拍照人脸匹配

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2019-12-20 DOI: 10.1109/IJCB48548.2020.9304906

Vítor Albiero, Nisha Srinivas, Esteban Villalobos, Jorge Perez-Facuse, Robert Rosenthal, D. Mery, K. Ricanek, K. Bowyer

{"title":"Identity Document to Selfie Face Matching Across Adolescence","authors":"Vítor Albiero, Nisha Srinivas, Esteban Villalobos, Jorge Perez-Facuse, Robert Rosenthal, D. Mery, K. Ricanek, K. Bowyer","doi":"10.1109/IJCB48548.2020.9304906","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304906","url":null,"abstract":"Matching live images (“selfies”) to images from ID documents is a problem that can arise in various applications. A challenging instance of the problem arises when the face image on the ID document is from early adolescence and the live image is from later adolescence. We explore this problem using a private dataset called Chilean Young Adult (CHIYA) dataset, where we match live face images taken at age 18–19 to face images on scanned ID documents created at ages 9 to 18. State-of-the-art deep learning face matchers (e.g., ArcFace) have relatively poor accuracy for document-to-selfie face matching. To achieve higher accuracy, we fine-tune the best available open-source model with triplet loss for a few-shot learning. Experiments show that our approach achieves higher accuracy than the DocFace+ model recently developed for this problem. Our fine-tuned model was able to improve the true acceptance rate for the most difficult (largest age span) subset from 62.92% to 96.67% at a false acceptance rate of 0.01%. Our fine-tuned model is available for use by other researchers.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116215190","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: 6

Fingerprint Spoof Detection: Temporal Analysis of Image Sequence 指纹欺骗检测:图像序列的时间分析

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2019-12-17 DOI: 10.1109/IJCB48548.2020.9304921

T. Chugh, Anil K. Jain

引用次数: 3

Fingerprint Synthesis: Search with 100 Million Prints 指纹合成:搜索1亿个指纹

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2019-12-16 DOI: 10.1109/IJCB48548.2020.9304885

Vishesh Mistry, Joshua J. Engelsma, Anil K. Jain

{"title":"Fingerprint Synthesis: Search with 100 Million Prints","authors":"Vishesh Mistry, Joshua J. Engelsma, Anil K. Jain","doi":"10.1109/IJCB48548.2020.9304885","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304885","url":null,"abstract":"Evaluation of large-scale fingerprint search algorithms has been limited due to lack of publicly available datasets. To address this problem, we utilize a Generative Adversarial Network (GAN) to synthesize a fingerprint dataset consisting of 100 million fingerprint images. In contrast to existing fingerprint synthesis algorithms, we incorporate an identity loss which guides the generator to synthesize fingerprints corresponding to more distinct identities. The characteristics of our synthesized fingerprints are shown to be more similar to real fingerprints than existing meth- ods via eight different metrics (minutiae count - block and template, minutiae direction - block and template, minutiae convex hull area, minutiae spatial distribution, block minutiae quality distribution, and NFIQ 2.0 scores). Additionally, the synthetic fingerprints based on our approach are shown to be more distinct than synthetic fingerprints based on published methods through search results and imposter distribution statistics. Finally, we report for the first time in open literature, search accuracy against a gallery of 1 00 million fingerprints (NIST SD4 Rank-1 accuracy of 89.7%).","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126805644","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: 20

White-Box Evaluation of Fingerprint Matchers: Robustness to Minutiae Perturbations 指纹匹配器的白盒评估:对微小扰动的鲁棒性

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2019-09-02 DOI: 10.1109/IJCB48548.2020.9304918

Steven A. Grosz, Joshua J. Engelsma, N. Paulter, Anil K. Jain

{"title":"White-Box Evaluation of Fingerprint Matchers: Robustness to Minutiae Perturbations","authors":"Steven A. Grosz, Joshua J. Engelsma, N. Paulter, Anil K. Jain","doi":"10.1109/IJCB48548.2020.9304918","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304918","url":null,"abstract":"Prevailing evaluations of fingerprint recognition systems have been performed as end-to-end black-box tests of fingerprint identification or authentication accuracy. However, performance of the end-to-end system is subject to errors arising in any of its constituent modules, including: fingerprint scanning, preprocessing, feature extraction, and matching. Conversely, white-box evaluations provide a more granular evaluation by studying the individual subcomponents of a system. While a few studies have conducted stand-alone evaluations of the fingerprint reader and feature extraction modules of fingerprint recognition systems, little work has been devoted towards white-box evaluations of the fingerprint matching module. We report results of a controlled, white-box evaluation of one open-source and two commercial-off-the-shelf (COTS) minutiae-based matchers in terms of their robustness against controlled perturbations (random noise and non-linear distortions) introduced into the input minutiae feature sets. Our white-box evaluations reveal that the performance of fingerprint minutiae matchers are more susceptible to non-linear distortion and missing minutiae than spurious minutiae and small positional displacements of the minutiae locations.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126258213","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: 6

AdvFaces: Adversarial Face Synthesis AdvFaces:对抗性人脸合成

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2019-08-14 DOI: 10.1109/IJCB48548.2020.9304898

Debayan Deb, Jianbang Zhang, Anil K. Jain

引用次数: 87

Backdooring Convolutional Neural Networks via Targeted Weight Perturbations 基于目标权扰动的反向卷积神经网络

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2018-12-07 DOI: 10.1109/IJCB48548.2020.9304875

Jacob Dumford, W. Scheirer

{"title":"Backdooring Convolutional Neural Networks via Targeted Weight Perturbations","authors":"Jacob Dumford, W. Scheirer","doi":"10.1109/IJCB48548.2020.9304875","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304875","url":null,"abstract":"We present a new white-box backdoor attack that exploits a vulnerability of convolutional neural networks (CNNs). In particular, we examine the application of facial recognition. Deep learning techniques are at the top of the game for facial recognition, which means they have now been implemented in many production-level systems. Alarmingly, unlike other commercial technologies such as operating systems and network devices, deep learning-based facial recognition algorithms are not presently designed with security requirements or audited for security vulnerabilities before deployment. Given how young the technology is and how abstract many of the internal workings of these algorithms are, neural network-based facial recognition systems are prime targets for security breaches. As more and more of our personal information begins to be guarded by facial recognition (e.g., the iPhone X), exploring the security vulnerabilities of these systems from a penetration testing standpoint is crucial. Along these lines, we describe a general methodology for backdooring CNNs via targeted weight perturbations. Using a five-layer CNN and ResNet-50 as case studies, we show that an attacker is able to significantly increase the chance that inputs they supply will be falsely accepted by a CNN while simultaneously preserving the error rates for legitimate enrolled classes.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123032319","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: 85