2019 International Conference on Biometrics (ICB)最新文献_第9页

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning FaceQnet:基于深度学习的人脸识别质量评估

2019 International Conference on Biometrics (ICB) Pub Date : 2019-04-03 DOI: 10.1109/ICB45273.2019.8987255

J. Hernandez-Ortega, Javier Galbally, Julian Fierrez, Rudolf Haraksim, Laurent Beslay

{"title":"FaceQnet: Quality Assessment for Face Recognition based on Deep Learning","authors":"J. Hernandez-Ortega, Javier Galbally, Julian Fierrez, Rudolf Haraksim, Laurent Beslay","doi":"10.1109/ICB45273.2019.8987255","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987255","url":null,"abstract":"In this paper we develop a Quality Assessment approach for face recognition based on deep learning. The method consists of a Convolutional Neural Network, FaceQnet, that is used to predict the suitability of a specific input image for face recognition purposes. The training of FaceQnet is done using the VGGFace2 database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images with quality information related to their ICAO compliance level. The groundtruth quality labels are obtained using FaceNet to generate comparison scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making it capable of returning a numerical quality measure for each input image. Finally, we verify if the FaceQnet scores are suitable to predict the expected performance when employing a specific image for face recognition with a COTS face recognition system. Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development. FaceQnet is publicly available in GitHub1.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128360543","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: 100

OGCTL: Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition 基于集成深度网络的姿态不变人脸识别的闭塞引导紧凑模板学习

2019 International Conference on Biometrics (ICB) Pub Date : 2019-03-12 DOI: 10.1109/ICB45273.2019.8987272

Yuhang Wu, I. Kakadiaris

引用次数: 0

The Unconstrained Ear Recognition Challenge 2019 2019年无约束耳识别挑战赛

2019 International Conference on Biometrics (ICB) Pub Date : 2019-03-11 DOI: 10.1109/ICB45273.2019.8987337

Ž. Emeršič, S. V. A. Kumar, B. Harish, Weronika Gutfeter, J. Khiarak, A. Pacut, E. Hansley, Maurício Pamplona Segundo, Sudeep Sarkar, Hyeon-Nam Park, G. Nam, Ig-Jae Kim, S. G. Sangodkar, Umit Kacar, M. Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, H. K. Ekenel, D. P. Chowdhury, Sambit Bakshi, B. Majhi, P. Peer, V. Štruc

{"title":"The Unconstrained Ear Recognition Challenge 2019","authors":"Ž. Emeršič, S. V. A. Kumar, B. Harish, Weronika Gutfeter, J. Khiarak, A. Pacut, E. Hansley, Maurício Pamplona Segundo, Sudeep Sarkar, Hyeon-Nam Park, G. Nam, Ig-Jae Kim, S. G. Sangodkar, Umit Kacar, M. Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, H. K. Ekenel, D. P. Chowdhury, Sambit Bakshi, B. Majhi, P. Peer, V. Štruc","doi":"10.1109/ICB45273.2019.8987337","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987337","url":null,"abstract":"This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person recognition from ear images captured in uncontrolled settings. The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i.e. gender and ethnicity. Research groups from 12 institutions entered the competition and submitted a total of 13 recognition approaches ranging from descriptor-based methods to deep-learning models. The majority of submissions focused on ensemble based methods combining either representations from multiple deep models or hand-crafted with learned image descriptors. Our analysis shows that methods incorporating deep learning models clearly outperform techniques relying solely on hand-crafted descriptors, even though both groups of techniques exhibit similar behavior when it comes to robustness to various covariates, such presence of occlusions, changes in (head) pose, or variability in image resolution. The results of the challenge also show that there has been considerable progress since the first UERC in 2017, but that there is still ample room for further research in this area.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997915","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

RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance 基于无监督对抗不变性的鲁棒表示攻击检测

2019 International Conference on Biometrics (ICB) Pub Date : 2019-03-08 DOI: 10.1109/ICB45273.2019.8987276

Ayush Jaiswal, Shuai Xia, I. Masi, Wael AbdAlmageed

引用次数: 15

Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN) 视频人脸识别:组件特征聚合网络(C-FAN)

2019 International Conference on Biometrics (ICB) Pub Date : 2019-02-19 DOI: 10.1109/ICB45273.2019.8987385

Sixue Gong, Yichun Shi, N. Kalka, Anil K. Jain

引用次数: 39

Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dual-stream Convolutional Neural Network 基于双流卷积神经网络局部二值编码模式正交组合的野外眼周识别

2019 International Conference on Biometrics (ICB) Pub Date : 2019-02-18 DOI: 10.1109/ICB45273.2019.8987278

L. Tiong, A. Teoh, Yunli Lee

引用次数: 12

Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone* Users via Deep Temporal Features 行动比(通过)话语更响亮:智能手机用户通过深度时间特征的被动认证

2019 International Conference on Biometrics (ICB) Pub Date : 2019-01-16 DOI: 10.1109/ICB45273.2019.8987433

Debayan Deb, A. Ross, Anil K. Jain, K. Prakah-Asante, K. Prasad

引用次数: 35

Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier 泛化指纹欺骗检测器:学习一类分类器

2019 International Conference on Biometrics (ICB) Pub Date : 2019-01-13 DOI: 10.1109/ICB45273.2019.8987319

Joshua J. Engelsma, Anil K. Jain

{"title":"Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier","authors":"Joshua J. Engelsma, Anil K. Jain","doi":"10.1109/ICB45273.2019.8987319","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987319","url":null,"abstract":"Prevailing fingerprint recognition systems are vulnerable to spoof attacks. To mitigate these attacks, automated spoof detectors are trained to distinguish a set of live or bona fide fingerprints from a set of known spoof fingerprints. Despite their success, spoof detectors remain vulnerable when exposed to attacks from spoofs made with materials not seen during training of the detector. To alleviate this shortcoming, we approach spoof detection as a one-class classification problem. The goal is to train a spoof detector on only the live fingerprints such that once the concept of \"live\" has been learned, spoofs of any material can be rejected. We accomplish this through training multiple generative adversarial networks (GANS) on live fingerprint images acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint reader. Our experimental results, conducted on 5.5K spoof images (from 12 materials) and 11.8K live images show that the proposed approach improves the cross-material spoof detection performance over state-of-the-art one-class and binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing materials, respectively.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126811186","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: 45

Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples 重新访问无学习虹膜分割:对视频样本快速体积操作的第一步

2019 International Conference on Biometrics (ICB) Pub Date : 2019-01-06 DOI: 10.1109/ICB45273.2019.8987377

Jeffery Kinnison, Mateusz Trokielewicz, Camila Carballo, A. Czajka, W. Scheirer

{"title":"Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples","authors":"Jeffery Kinnison, Mateusz Trokielewicz, Camila Carballo, A. Czajka, W. Scheirer","doi":"10.1109/ICB45273.2019.8987377","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987377","url":null,"abstract":"Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation. In many cases, iris biometrics acquisition equipment takes a number of images in sequence and combines the segmentation and matching results for each image to strengthen the result. To date, segmentation has occurred in 2D, operating on each image individually. But such methodologies, while powerful, do not take advantage of potential gains in performance afforded by treating sequential images as volumetric data. As a first step in this direction, we apply the Flexible Learning-Free Reconstructoin of Neural Volumes (FLoRIN) framework, an open source segmentation and reconstruction framework originally designed for neural microscopy volumes, to volumetric segmentation of iris videos. Further, we introduce a novel dataset of near-infrared iris videos, in which each subject’s pupil rapidly changes size due to visible-light stimuli, as a test bed for FLoRIN. We compare the matching performance for iris masks generated by FLoRIN, deep-learning-based (SegNet), and Daugman’s (OSIRIS) iris segmentation approaches. We show that by incorporating volumetric information, FLoRIN achieves a factor of 3.6 to an order of magnitude increase in throughput with only a minor drop in subject matching performance. We also demonstrate that FLoRIN-based iris segmentation maintains this speedup on low-resource hardware, making it suitable for embedded biometrics systems.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126495708","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: 13

Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks 虹膜识别与图像分割采用再训练现成的深度神经网络

2019 International Conference on Biometrics (ICB) Pub Date : 2019-01-04 DOI: 10.1109/ICB45273.2019.8987299

Daniel Kerrigan, Mateusz Trokielewicz, A. Czajka, K. Bowyer

{"title":"Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks","authors":"Daniel Kerrigan, Mateusz Trokielewicz, A. Czajka, K. Bowyer","doi":"10.1109/ICB45273.2019.8987299","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987299","url":null,"abstract":"This paper offers three new, open-source, deep learning-based iris segmentation methods, and the methodology how to use irregular segmentation masks in a conventional Gabor-wavelet-based iris recognition. To train and validate the methods, we used a wide spectrum of iris images acquired by different teams and different sensors and offered publicly, including data taken from CASIA-Iris-Interval-v4, BioSec, ND-Iris-0405, UBIRIS, Warsaw-BioBase-Post-Mortem-Iris v2.0 (post-mortem iris images), and ND-TWINS-2009-2010 (iris images acquired from identical twins). This varied training data should increase the generalization capabilities of the proposed segmentation techniques. In database-disjoint training and testing, we show that deep learning-based segmentation outperforms the conventional (OSIRIS) segmentation in terms of Intersection over Union calculated between the obtained results and manually annotated ground-truth. Interestingly, the Gabor-based iris matching is not always better when deep learning-based segmentation is used, and is on par with the method employing Daugman’s based segmentation.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131273751","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