2019 International Conference on Biometrics (ICB)最新文献

Likelihood Ratio based Loss to finetune CNNs for Very Low Resolution Face Verification 基于似然比损失的极低分辨率人脸验证微调cnn

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987249

Dan Zeng, R. Veldhuis, L. Spreeuwers, Qijun Zhao

引用次数: 1

OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender 基于OU-ISIR可穿戴传感器的步态挑战:年龄和性别

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987235

T. N. Thanh, Yuichi Hattori, Md. Atiqur Rahman Ahad, Anindya Das Antar, Masud Ahmed, D. Muramatsu, Yasushi Makihara, Y. Yagi, Sozo Inoue, Tahera Hossain

引用次数: 13

A novel scheme to address the fusion uncertainty in multi-modal continuous authentication schemes on mobile devices 一种解决移动设备上多模态连续认证方案中融合不确定性的新方案

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987390

Max Smith-Creasey, M. Rajarajan

引用次数: 6

FLDet: A CPU Real-time Joint Face and Landmark Detector FLDet:一种CPU实时联合人脸和地标检测器

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987289

Chubin Zhuang, Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Jinqiao Wang, S. Li

{"title":"FLDet: A CPU Real-time Joint Face and Landmark Detector","authors":"Chubin Zhuang, Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Jinqiao Wang, S. Li","doi":"10.1109/ICB45273.2019.8987289","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987289","url":null,"abstract":"Face detection and alignment are considered as two independent tasks and conducted sequentially in most face applications. However, these two tasks are highly related and they can be integrated into a single model. In this paper, we propose a novel single-shot detector for joint face detection and alignment, namely FLDet, with remarkable performance on both speed and accuracy. Specifically, the FLDet consists of three main modules: Rapidly Digested Backbone (RDB), Lightweight Feature Pyramid Network (LFPN) and Multi-task Detection Module (MDM). The RDB quickly shrinks the spatial size of feature maps to guarantee the CPU real-time speed. The LFPN integrates different detection layers in a top-down fashion to enrich the feature of low-level layers with little extra time overhead. The MDM jointly performs face and landmark detection over different layers to handle faces of various scales. Besides, we introduce a new data augmentation strategy to take full usage of the face alignment dataset. As a result, the proposed FLDet can run at 20 FPS on a single CPU core and 120 FPS using a GPU for VGA-resolution images. Notably, the FLDet can be trained end-to-end and its inference time is invariant to the number of faces. We achieve competitive results on both face detection and face alignment benchmark datasets, including AFW, PASCAL FACE, FDDB and AFLW.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134024941","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

Suppressing Gender and Age in Face Templates Using Incremental Variable Elimination 用增量变量消除法抑制人脸模板中的性别和年龄

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987331

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

{"title":"Suppressing Gender and Age in Face Templates Using Incremental Variable Elimination","authors":"Philipp Terhörst, N. Damer, Florian Kirchbuchner, Arjan Kuijper","doi":"10.1109/ICB45273.2019.8987331","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987331","url":null,"abstract":"Recent research on soft-biometrics showed that more information than just the person’s identity can be deduced from biometric data. Using face templates only, information about gender, age, ethnicity, health state of the person, and even the sexual orientation can be automatically obtained. Since for most applications these templates are expected to be used for recognition purposes only, this raises major privacy issues. Previous work addressed this problem purely on image level regarding function creep attackers without knowledge about the systems privacy mechanism. In this work, we propose a soft-biometric privacy enhancing approach that reduces a given biometric template by eliminating its most important variables for predicting soft-biometric attributes. Training a decision tree ensemble allows deriving a variable importance measure that is used to incrementally eliminate variables that allow predicting sensitive attributes. Unlike previous work, we consider a scenario of function creep attackers with explicit knowledge about the privacy mechanism and evaluated our approach on a publicly available database. The experiments were conducted to eight baseline solutions. The results showed that in many cases IVE is able to suppress gender and age to a high degree with a negligible loss of the templates recognition ability. Contrary to previous work, which is limited to the suppression of binary (gender) attributes, IVE is able, by design, to suppress binary, categorical, and continuous attributes.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117070300","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: 28

Face Sketch Colorization via Supervised GANs 基于监督gan的人脸素描着色

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987296

S. RamyaY., Soumyadeep Ghosh, Mayank Vatsa, Richa Singh

引用次数: 2

SEFD: A Simple and Effective Single Stage Face Detector SEFD:一个简单有效的单级人脸检测器

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987231

Lei Shi, Xiang Xu, I. Kakadiaris

引用次数: 2

Deep Contactless Fingerprint Unwarping 深度非接触式指纹解锁

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987292

Ali Dabouei, Sobhan Soleymani, J. Dawson, N. Nasrabadi

{"title":"Deep Contactless Fingerprint Unwarping","authors":"Ali Dabouei, Sobhan Soleymani, J. Dawson, N. Nasrabadi","doi":"10.1109/ICB45273.2019.8987292","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987292","url":null,"abstract":"Contactless fingerprints have emerged as a convenient, inexpensive, and hygienic way of capturing fingerprint samples. However, cross-matching contactless fingerprints to the legacy contact-based fingerprints is a challenging task due to the elastic and perspective distortion between the two modalities. Current cross-matching methods merely rectify the elastic distortion of the contact-based samples to reduce the geometric mismatch and ignore the perspective distortion of contactless fingerprints. Adopting classical deformation correction techniques to compensate for the perspective distortion requires a large number of minutiae-annotated contactless fingerprints. However, annotating minutiae of contactless samples is a labor-intensive and inaccurate task especially for regions which are severely distorted by the perspective projection. In this study, we propose a deep model to rectify the perspective distortion of contactless fingerprints by combining a rectification and a ridge enhancement network. The ridge enhancement network provides indirect supervision for training the rectification network and removes the need for the ground truth values of the estimated warp parameters. Comprehensive experiments using two public datasets of contactless fingerprints show that the proposed unwarping approach, on average, results in a 17% increase in the number of detectable minutiae from contactless fingerprints. Consequently, the proposed model achieves the equal error rate of 7.71% and Rank-1 accuracy of 61.01% on the challenging dataset of ‘2D/3D’ fingerprints.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129903369","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

Does Generative Face Completion Help Face Recognition? 生成人脸补全是否有助于人脸识别?

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987388

Joe Mathai, I. Masi, Wael AbdAlmageed

{"title":"Does Generative Face Completion Help Face Recognition?","authors":"Joe Mathai, I. Masi, Wael AbdAlmageed","doi":"10.1109/ICB45273.2019.8987388","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987388","url":null,"abstract":"Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information. Biometric systems such as recent deep face recognition models are not immune to obstructions or other objects covering parts of the face. While most of the current face recognition methods are not optimized to handle occlusions, there have been a few attempts to improve robustness directly in the training stage. Unlike those, we propose to study the effect of generative face completion on the recognition. We offer a face completion encoder-decoder, based on a convolutional operator with a gating mechanism, trained with an ample set of face occlusions. To systematically evaluate the impact of realistic occlusions on recognition, we propose to play the occlusion game: we render 3D objects onto different face parts, providing precious knowledge of what the impact is of effectively removing those occlusions. Extensive experiments on the Labeled Faces in the Wild (LFW), and its more difficult variant LFW-BLUFR, testify that face completion is able to partially restore face perception in machine vision systems for improved recognition.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128664281","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: 25

On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks 激光散斑对比成像和深度神经网络检测已知和未知指纹表示攻击的有效性研究

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987428

H. Mirzaalian, Mohamed E. Hussein, W. Abd-Almageed

{"title":"On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks","authors":"H. Mirzaalian, Mohamed E. Hussein, W. Abd-Almageed","doi":"10.1109/ICB45273.2019.8987428","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987428","url":null,"abstract":"Fingerprint presentation attack detection (FPAD) is becoming an increasingly challenging problem due to the continuous advancement of attack techniques, which generate \"realistic-looking\" fake fingerprint presentations. Recently, laser speckle contrast imaging (LSCI) has been introduced as a new sensing modality for FPAD. LSCI has the interesting characteristic of capturing the blood flow under the skin surface. Toward studying the importance and effectiveness of LSCI for FPAD, we conduct a comprehensive study using different patch-based deep neural network architectures. Our studied architectures include 2D and 3D convo-lutional networks as well as a recurrent network using long short-term memory (LSTM) units. The study demonstrates that strong FPAD performance can be achieved using LSCI. We evaluate the different models over a new large dataset. The dataset consists of 3743 bona fide samples, collected from 335 unique subjects, and 218 presentation attack samples, including six different types of attacks. To examine the effect of changing the training and testing sets, we conduct a 3-fold cross validation evaluation. To examine the effect of the presence of an unseen attack, we apply a leave-one-attack out strategy. The FPAD classification results of the networks, which are separately optimized and tuned for the temporal and spatial patch-sizes, indicate that the best performance is achieved by LSTM.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128221255","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