2020 IEEE International Joint Conference on Biometrics (IJCB)最新文献_第7页

Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN 基于多任务CNN结合纹理、细节和频谱的指纹特征提取

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-08-27 DOI: 10.1109/IJCB48548.2020.9304861

Ai Takahashi, Yoshinori Koda, Koichi Ito, T. Aoki

引用次数: 19

Assessing the Quality of Swipe Interactions for Mobile Biometric Systems 评估移动生物识别系统的滑动交互质量

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-08-27 DOI: 10.1109/IJCB48548.2020.9304858

Marco Santopietro, R. Vera-Rodríguez, R. Guest, A. Morales, A. Acien

引用次数: 2

How Do the Hearts of Deep Fakes Beat? Deep Fake Source Detection via Interpreting Residuals with Biological Signals Deep Fakes的心脏是如何跳动的?基于生物信号残差解释的深度假源检测

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-08-26 DOI: 10.1109/IJCB48548.2020.9304909

U. Ciftci, Ilke Demir, L. Yin

{"title":"How Do the Hearts of Deep Fakes Beat? Deep Fake Source Detection via Interpreting Residuals with Biological Signals","authors":"U. Ciftci, Ilke Demir, L. Yin","doi":"10.1109/IJCB48548.2020.9304909","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304909","url":null,"abstract":"Fake portrait video generation techniques have been posing a new threat to the society with photorealistic deep fakes for political propaganda, celebrity imitation, forged evidences, and other identity related manipulations. Following these generation techniques, some detection approaches have also been proved useful due to their high classification accuracy. Nevertheless, almost no effort was spent to track down the source of deep fakes. We propose an approach not only to separate deep fakes from real videos, but also to discover the specific generative model behind a deep fake. Some pure deep learning based approaches try to classify deep fakes using CNNs where they actually learn the residuals of the generator. We believe that these residuals contain more information and we can reveal these manipulation artifacts by disentangling them with biological signals. Our key observation yields that the spatiotemporal patterns in biological signals can be conceived as a representative projection of residuals. To justify this observation, we extract PPG cells from real and fake videos and feed these to a state-of-the-art classification network for detecting the generative model per video. Our results indicate that our approach can detect fake videos with 97.29% accuracy, and the source model with 93.39% accuracy.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"13 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116772832","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: 48

Cross-Spectral Periocular Recognition with Conditional Adversarial Networks 条件对抗网络的交叉光谱眼周识别

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-08-26 DOI: 10.1109/IJCB48548.2020.9304899

Kevin Hernandez-Diaz, F. Alonso-Fernandez, J. Bigün

{"title":"Cross-Spectral Periocular Recognition with Conditional Adversarial Networks","authors":"Kevin Hernandez-Diaz, F. Alonso-Fernandez, J. Bigün","doi":"10.1109/IJCB48548.2020.9304899","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304899","url":null,"abstract":"This work addresses the challenge of comparing periocular images captured in different spectra, which is known to produce significant drops in performance in comparison to operating in the same spectrum. We propose the use of Conditional Generative Adversarial Networks, trained to convert periocular images between visible and near-infrared spectra, so that biometric verification is carried out in the same spectrum. The proposed setup allows the use of existing feature methods typically optimized to operate in a single spectrum. Recognition experiments are done using a number of off-the-shelf periocular comparators based both on hand-crafted features and CNN descriptors. Using the Hong Kong Polytechnic University Cross-Spectral Iris Images Database (PolyU) as benchmark dataset, our experiments show that cross-spectral performance is substantially improved if both images are converted to the same spectrum, in comparison to matching features extracted from images in different spectra. In addition to this, we fine-tune a CNN based on the ResNet50 architecture, obtaining a cross-spectral periocular performance of EER=l%, and GAR>99% @ FAR=l%, which is comparable to the state-of-the-art with the PolyU database.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127559695","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: 9

Cross-Domain Identification for Thermal-to-Visible Face Recognition 热-可见人脸识别的跨域识别

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-08-19 DOI: 10.1109/IJCB48548.2020.9304937

Cedric Nimpa Fondje, Shuowen Hu, Nathan J. Short, B. Riggan

{"title":"Cross-Domain Identification for Thermal-to-Visible Face Recognition","authors":"Cedric Nimpa Fondje, Shuowen Hu, Nathan J. Short, B. Riggan","doi":"10.1109/IJCB48548.2020.9304937","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304937","url":null,"abstract":"Recent advances in domain adaptation, especially those applied to heterogeneous facial recognition, typically rely upon restrictive Euclidean loss functions (e.g., L2 norm) which perform best when images from two different domains (e.g., visible and thermal) are co-registered and temporally synchronized. This paper proposes a novel domain adaptation framework that combines a new feature mapping sub-network with existing deep feature models, which are based on modified network architectures (e.g., VGG16 or Resnet50). This framework is optimized by introducing new cross-domain identity and domain invariance lossfunctions for thermal-to-visible face recognition, which alleviates the requirement for precisely co-registered and synchronized imagery. We provide extensive analysis of both features and loss functions used, and compare the proposed domain adaptation framework with state-of-the-art feature based domain adaptation models on a difficult dataset containing facial imagery collected at varying ranges, poses, and expressions. Moreover, we analyze the viability of the proposed framework for more challenging tasks, such as non-frontal thermal-to-visible face recognition.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127539127","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: 17

Open Source Iris Recognition Hardware and Software with Presentation Attack Detection 开源虹膜识别硬件和软件与表示攻击检测

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-08-19 DOI: 10.1109/IJCB48548.2020.9304869

Zhaoyuan Fang, A. Czajka

引用次数: 11

Domain Private and Agnostic Feature for Modality Adaptive Face Recognition 模态自适应人脸识别的领域私有和不可知特征

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-08-10 DOI: 10.1109/IJCB48548.2020.9304895

Ying Xu, Lei Zhang, Qingyan Duan

{"title":"Domain Private and Agnostic Feature for Modality Adaptive Face Recognition","authors":"Ying Xu, Lei Zhang, Qingyan Duan","doi":"10.1109/IJCB48548.2020.9304895","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304895","url":null,"abstract":"Heterogeneous face recognition is a challenging task due to the large modality discrepancy and insufficient cross-modal samples. Most existing works focus on discriminative feature transformation, metric learning and cross-modal face synthesis. However, the fact that cross-modal faces are always coupled by domain (modality) and identity information has received little attention. Therefore, how to learn and utilize the domain-private feature and domain-agnostic feature for modality adaptive face recognition is the focus of this work. Specifically, this paper proposes a Feature Aggregation Network (FAN), which includes disentangled representation module (DRM), feature fusion module (FFM) and adaptive penalty metric (APM) learning session. First, in DRM, two subnetworks, i.e. domain-private network and domain-agnostic network are specially designed for learning modality features and identity features, respectively. Second, in FFM, the identity features are fused with domain features to achieve cross-modal bidirectional identity feature transformation, which, to a large extent, further disentangles the modality information and identity information. Third, considering that the distribution imbalance between easy and hard pairs exists in cross-modal datasets, which increases the risk of model bias, the identity preserving guided metric learning with adaptive hard pairs penalization is proposed in our FAN. The proposed APM also guarantees the cross-modality intra-class compactness and inter-class separation. Extensive experiments on benchmark cross-modal face datasets show that our FAN outperforms SOTA methods.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130215037","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: 2

Gender and Ethnicity Classification based on Palmprint and Palmar Hand Images from Uncontrolled Environment 基于非受控环境掌纹和手掌图像的性别和种族分类

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-08-06 DOI: 10.1109/IJCB48548.2020.9304907

Wojciech Michal Matkowski, A. Kong

引用次数: 4

Resist: Reconstruction of irises from templates 抵抗:从模板重建虹膜

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-07-31 DOI: 10.1109/IJCB48548.2020.9304912

Sohaib Ahmad, Benjamin Fuller

{"title":"Resist: Reconstruction of irises from templates","authors":"Sohaib Ahmad, Benjamin Fuller","doi":"10.1109/IJCB48548.2020.9304912","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304912","url":null,"abstract":"Iris recognition systems transform an iris image into a feature vector. The seminal pipeline segments an image into iris and non-iris pixels, normalizes this region into a fixed-dimension rectangle, and extracts features which are stored and called a template (Daugman, 2009). This template is stored on a system. A future reading of an iris can be transformed and compared against template vectors to determine or verify the identity of an individual. As templates are often stored together, they are a valuable target to an attacker. We show how to invert templates across a variety of iris recognition systems. Our inversion is based on a convolutional neural network architecture we call RESIST (REconStructing IriSes from Templates). We apply RESIST to a traditional Gabor filter pipeline, to a DenseNet (Huang et al., CVPR 2017)feature extractor, and to a DenseNet architecture that works without normalization. Both DenseNet feature extractors are based on the recent ThirdEye recognition system (Ahmad and Fuller, BTAS 2019). When training and testing using the ND-0405 dataset, reconstructed images demonstrate a rank-1 accuracy of 100%, 76%, and 96% respectively for the three pipelines. The core of our approach is similar to an autoencoder. To obtain high accuracy this core is integrated into an adversarial network (Goodfellow et al., NeurIPS, 2014)","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130026789","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

Swipe Dynamics as a Means of Authentication: Results From a Bayesian Unsupervised Approach 滑动动态作为一种认证手段:来自贝叶斯无监督方法的结果

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-07-27 DOI: 10.1109/IJCB48548.2020.9304876

Parker Lamb, Alexander Millar, Ramon Fuentes

{"title":"Swipe Dynamics as a Means of Authentication: Results From a Bayesian Unsupervised Approach","authors":"Parker Lamb, Alexander Millar, Ramon Fuentes","doi":"10.1109/IJCB48548.2020.9304876","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304876","url":null,"abstract":"The field of behavioural biometrics stands as an appealing alternative to more traditional biometric systems due to the ease of use from a user perspective and potential robustness to presentation attacks. This paper focuses its attention to a specific type of behavioural biometric utilising swipe dynamics, also referred to as touch gestures. In touch gesture authentication, a user swipes across the touchscreen of a mobile device to perform an authentication attempt. A key characteristic of touch gesture authentication and new behavioural biometrics in general is the lack of available data to train and validate models. From a machine learning perspective, this presents the classic curse of dimensionality problem and the methodology presented here focuses on Bayesian unsupervised models as they are well suited to such conditions. This paper presents results from a set of experiments consisting of 38 sessions with labelled ‘victim’ as well as blind and over-the-shoulder presentation attacks. Three models are compared using this dataset; two single-mode models: a shrunk covariance estimate and a Bayesian Gaussian distribution, as well as a Bayesian non-parametric infinite mixture of Gaussians, modelled as a Dirichlet Process. Equal error rates (EER) for the three models are compared and attention is paid to how these vary across the two single-mode models at differing numbers of enrolment samples.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128959532","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: 2