2020 IEEE International Joint Conference on Biometrics (IJCB)最新文献_第5页

Facial landmark detection on thermal data via fully annotated visible-to-thermal data synthesis 通过完全注释的可见热数据合成对热数据进行面部地标检测

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304854

Khawla Mallat, J. Dugelay

{"title":"Facial landmark detection on thermal data via fully annotated visible-to-thermal data synthesis","authors":"Khawla Mallat, J. Dugelay","doi":"10.1109/IJCB48548.2020.9304854","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304854","url":null,"abstract":"Thermal imaging has substantially evolved, during the recent years, to be established as a complement, or even occasionally as an alternative to conventional visible light imaging, particularly for face analysis applications. Facial landmark detection is a crucial prerequisite for facial image processing. Given the upswing of deep learning based approaches, the performance of facial landmark detection has been significantly improved. However, this uprise is merely limited to visible spectrum based face analysis tasks, as there are only few research works on facial landmark detection in thermal spectrum. This limitation is mainly due to the lack of available thermal face databases provided with full facial landmark annotations. In this paper, we propose to tackle this data shortage by converting existing face databases, designed for facial landmark detection task, from visible to thermal spectrum that will share the same provided facial landmark annotations. Using the synthesized thermal databases along with the facial landmark annotations, two different models are trained using active appearance models and deep alignment network. Evaluating the models trained on synthesized thermal data on real thermal data, we obtained facial landmark detection accuracy of 94.59% when tested on low quality thermal data and 95.63% when tested on high quality thermal data with a detection threshold of 0.15×IOD.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114504853","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: 4

Is Face Recognition Safe from Realizable Attacks? 人脸识别安全吗?

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304864

Sanjay Saha, T. Sim

引用次数: 2

Analysing the Performance of LSTMs and CNNs on 1310 nm Laser Data for Fingerprint Presentation Attack Detection lstm和cnn在1310nm激光指纹呈现攻击检测中的性能分析

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304888

Jascha Kolberg, Alexandru-Cosmin Vasile, M. Gomez-Barrero, C. Busch

{"title":"Analysing the Performance of LSTMs and CNNs on 1310 nm Laser Data for Fingerprint Presentation Attack Detection","authors":"Jascha Kolberg, Alexandru-Cosmin Vasile, M. Gomez-Barrero, C. Busch","doi":"10.1109/IJCB48548.2020.9304888","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304888","url":null,"abstract":"Due to the wide operational deployment of biometric recognition systems, presentation attacks targeting the capture device have become a severe threat. Especially for fingerprint recognition, a high number of different materials allows the creation of numerous presentation attack instruments (PAIs) in the form of full fake fingers and fingerprint overlays, which very much resemble the skin properties at fingertips. As a consequence, automated presentation attack detection (PAD) mechanisms are of utmost importance. Utilising a 1310 nm laser in a new capture device, we present an evaluation of three long short-term memory (LSTM) networks in comparison to eight convolutional neural networks (CNNs) on a database comprising over 22,000 samples and including 45 different PAI species. The LSTMs analyse temporal properties within a captured sequence in order to detect blood movement, while the CNNs take into account spatial properties within a single frame to focus on reflections by the PAI material. The results show that the diversity of PAI species is too big for a single classifier to correctly detect all presentation attacks. However, by fusing the scores from distinct algorithms, we can achieve a detection accuracy of 3.71% APCER for a convenient BPCER of 0.2%.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127196382","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

How Confident Are You in Your Estimate of a Human Age? Uncertainty-aware Gait-based Age Estimation by Label Distribution Learning 你对人类年龄的估计有多大信心?基于标签分布学习的不确定性步态年龄估计

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304914

Atsuya Sakata, Yasushi Makihara, Noriko Takemura, D. Muramatsu, Y. Yagi

引用次数: 9

Copyright 版权

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/ijcb48548.2020.9304915

引用次数: 0

Biometric Identification and Presentation-Attack Detection using Micro- and Macro-Movements of the Eyes 利用眼睛的微观和宏观运动进行生物识别和呈现攻击检测

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304900

Silvia Makowski, L. Jäger, Paul Prasse, T. Scheffer

引用次数: 8

LDM-DAGSVM: Learning Distance Metric via DAG Support Vector Machine for Ear Recognition Problem LDM-DAGSVM:基于DAG支持向量机学习距离度量的耳朵识别问题

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304871

Ibrahim Omara, Guangzhi Ma, E. Song

{"title":"LDM-DAGSVM: Learning Distance Metric via DAG Support Vector Machine for Ear Recognition Problem","authors":"Ibrahim Omara, Guangzhi Ma, E. Song","doi":"10.1109/IJCB48548.2020.9304871","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304871","url":null,"abstract":"Recently, the ear recognition system takes more increasingly interesting for many applications, especially, in immigration system, forensic, and surveillance applications. For face re-identification and image classification, metric learning has significantly improved machine learning accuracies by using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers. However, metric learning via SVM has not yet been investigated for the ear recognition problem. To achieve better generalization ability than the traditional previous classifiers, a novel framework for ear recognition is proposed based on learning distance metric (LDM) via SVM since the LDM and the directed acyclic graph SVM (DAGSVM) are two emerging techniques which perform outstanding in dealing with classification problems. This work considers metric learning for SVM by proposing a hybrid learning distance metric and directed acyclic graph SVM (LDM-DAGSVM) model for ear recognition system. Different from existing ear biometric methods, the proposed approach aims to learn a Mahalanobis distance metric via SVM to maximize the inter-class variations and minimize the intra-class variations, simultaneously. The experiments are conducted on complicated ear datasets and the results can achieve better performance compared with the state-of-the-art ear recognition methods. The proposed approach can get classification accuracy up to 98.79%, 98.70%, and 84.30% for AWE, AME and WPUT ear datasets, respectively.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134050629","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

DVRNet: Decoupled Visible Region Network for Pedestrian Detection 用于行人检测的解耦可见区域网络

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304883

Lei Shi, Charles Livermore, I. Kakadiaris

引用次数: 1

DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition 基于域的NIR-VIS异构人脸识别方法

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304884

Ha A. Le, I. Kakadiaris

{"title":"DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition","authors":"Ha A. Le, I. Kakadiaris","doi":"10.1109/IJCB48548.2020.9304884","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304884","url":null,"abstract":"Deep learning-based domain-invariant feature learning methods are advancing in near-infrared and visible (NIR-VIS) heterogeneous face recognition. However, these methods are prone to overfitting due to the large intra-class variation and the lack of NIR images for training. In this paper, we introduce Domain-Based Label Face (DBLFace), a learning approach based on the assumption that a subject is not represented by a single label but by a set of labels. Each label represents images of a specific domain. In particular, a set of two labels per subject, one for the NIR images and one for the VIS images, are used for training a NIR-VIS face recognition model. The classification of images into different domains reduces the intra-class variation and lessens the negative impact of data imbalance in training. To train a network with sets of labels, we introduce a domain-based angular margin loss and a maximum angular loss to maintain the inter-class discrepancy and to enforce the close relationship of labels in a set. Quantitative experiments confirm that DBLFace significantly improves the rank-1 identification rate by 6.7% on the EDGE20 dataset and achieves state-of-the-art performance on the CASIA NIR-VIS 2.0 dataset.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129778763","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: 4

Partial Fingerprint Verification via Spatial Transformer Networks 基于空间变压器网络的部分指纹验证

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304877

Zhiyuan He, Eryun Liu, Z. Xiang

引用次数: 6