2019 International Conference on Biometrics (ICB)最新文献_第2页

Seg-Edge Bilateral Constraint Network for Iris Segmentation 虹膜分割的Seg-Edge双边约束网络

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987405

Junxing Hu, Hui Zhang, Lihu Xiao, Jing Liu, Xingguang Li, Zhaofeng He, Ling Li

引用次数: 4

Face Anti-spoofing using Hybrid Residual Learning Framework 基于混合残差学习框架的人脸防欺骗

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987283

Usman Muhammad, A. Hadid

{"title":"Face Anti-spoofing using Hybrid Residual Learning Framework","authors":"Usman Muhammad, A. Hadid","doi":"10.1109/ICB45273.2019.8987283","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987283","url":null,"abstract":"Face spoofing attacks have received significant attention because of criminals who are developing different techniques such as warped photos, cut photos, 3D masks, etc. to easily fool the face recognition systems. In order to improve the security measures of biometric systems, deep learning models offer powerful solutions; but to attain the benefits of multilayer features remains a significant challenge. To alleviate this limitation, this paper presents a hybrid framework to build the feature representation by fusing ResNet with more discriminative power. First, two variants of the residual learning framework are selected as deep feature extractors to extract informative features. Second, the fullyconnected layers are used as separated feature descriptors. Third, PCA based Canonical correlation analysis (CCA) is proposed as a feature fusion strategy to combine relevant information and to improve the features’ discrimination capacity. Finally, the support vector machine (SVM) is used to construct the final representation of facial features. Experimental results show that our proposed framework achieves a state-of-the-art performance without finetuning, data augmentation or coding strategy on benchmark databases, namely the MSU mobile face spoof database and the CASIA face anti-spoofing database.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"os-53 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":"127846789","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: 15

Mobile Face Recognition Systems: Exploring Presentation Attack Vulnerability and Usability 移动人脸识别系统:探索表示攻击漏洞和可用性

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987404

H. Hofbauer, L. Debiasi, A. Uhl

引用次数: 2

On the Extent of Longitudinal Finger Rotation in Publicly Available Finger Vein Data Sets 公开可用的指静脉数据集中手指纵向旋转的程度

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987384

B. Prommegger, Christof Kauba, A. Uhl

{"title":"On the Extent of Longitudinal Finger Rotation in Publicly Available Finger Vein Data Sets","authors":"B. Prommegger, Christof Kauba, A. Uhl","doi":"10.1109/ICB45273.2019.8987384","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987384","url":null,"abstract":"Finger vein recognition deals with the identification of a subjects based on its venous pattern within the fingers. The majority of the publicly available finger vein data sets has been acquired with the help of scanner devices that capture a single finger from the palmar side using light transmission. Some of them are equipped with a contact surface or other structures to support in finger placement. However, these means are not able to prevent all possible types of finger misplacements, in particular longitudinal finger rotation can not be averted. It has been shown that this type of finger rotation results in a non-linear deformation of the vein structure, causing severe problems to finger vein recognition systems. So far it is not known if and to which extent this longitudinal finger rotation is present in publicly available finger vein data sets. This paper evaluates the presence of longitudinal finger rotation and its extent in four publicly available finger vein data sets and provides the estimated rotation angles to the scientific public. This additional information will increase the value of the evaluated data sets. To verify the correctness of the estimated rotation angles, we furthermore demonstrate that employing a simple rotation correction, using those rotation angles, improves the recognition performance.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"25 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":"126503469","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: 11

Cross-spectrum thermal to visible face recognition based on cascaded image synthesis 基于级联图像合成的跨光谱热到可见人脸识别

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987347

Khawla Mallat, N. Damer, F. Boutros, Arjan Kuijper, J. Dugelay

{"title":"Cross-spectrum thermal to visible face recognition based on cascaded image synthesis","authors":"Khawla Mallat, N. Damer, F. Boutros, Arjan Kuijper, J. Dugelay","doi":"10.1109/ICB45273.2019.8987347","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987347","url":null,"abstract":"Face synthesis from thermal to visible spectrum is fundamental to perform cross-spectrum face recognition as it simplifies its integration in existing commercial face recognition systems and enables manual face verification. In this paper, a new solution based on cascaded refinement networks is proposed. This method generates visible-like colored images of high visual quality without requiring large amounts of training data. By employing a contextual loss function during training, the proposed network is inherently scale and rotation invariant. We discuss the visual perception of the generated visible-like faces in comparison with recent works. We also provide an objective evaluation in terms of cross-spectrum face recognition, where the generated faces were compared against a gallery in visible spectrum using two state-of-the-art deep learning based face recognition systems. When compared to the recently published TV-GAN solution, the performance of the face recognition systems, OpenFace and LightCNN, was improved by a 42.48% (i.e. from 10.76% to 15.37%) and a 71.43% (i.e. from 33.606% to 57.612%), respectively.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"4 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":"121401697","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: 21

Deep Learning from 3DLBP Descriptors for Depth Image Based Face Recognition 基于深度图像人脸识别的3DLBP描述符深度学习

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987432

João Baptista Cardia Neto, A. Marana, C. Ferrari, S. Berretti, A. Bimbo

{"title":"Deep Learning from 3DLBP Descriptors for Depth Image Based Face Recognition","authors":"João Baptista Cardia Neto, A. Marana, C. Ferrari, S. Berretti, A. Bimbo","doi":"10.1109/ICB45273.2019.8987432","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987432","url":null,"abstract":"In this paper, we propose a new framework for face recognition from depth images, which is both effective and efficient. It consists of two main stages: First, a handcrafted low-level feature extractor is applied to the raw depth data of the face, thus extracting the corresponding descriptor images (DIs); Then, a not-so-deep (shallow) convolutional neural network (SCNN) has been designed that learns from the DIs. This architecture showed two main advantages over the direct application of deep-CNN (DCNN) to the depth images of the face: On the one hand, the DIs are capable of enriching the raw depth data, emphasizing relevant traits of the face, while reducing their acquisition noise. This resulted decisive in improving the learning capability of the network; On the other, the DIs capture low-level features of the face, thus playing the role for the SCNN as the first layers do in a DCNN architecture. In this way, the SCNN we have designed has much less layers and can be trained more easily and faster. Extensive experiments on low- and high-resolution depth face datasets confirmed us the above advantages, showing results that are comparable or superior to the state-of-the-art, using by far less training data, time, and memory occupancy of the network.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"84 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":"127965012","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

Understanding Confounding Factors in Face Detection and Recognition 理解人脸检测与识别中的混杂因素

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987419

Janet Anderson, C. Otto, Brianna Maze, N. Kalka, James A. Duncan

引用次数: 3

Crafting A Panoptic Face Presentation Attack Detector 制作一个全景面部呈现攻击检测器

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987257

Suril Mehta, A. Uberoi, Akshay Agarwal, Mayank Vatsa, Richa Singh

{"title":"Crafting A Panoptic Face Presentation Attack Detector","authors":"Suril Mehta, A. Uberoi, Akshay Agarwal, Mayank Vatsa, Richa Singh","doi":"10.1109/ICB45273.2019.8987257","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987257","url":null,"abstract":"With the advancements in technology and growing popularity of facial photo editing in the social media landscape, tools such as face swapping and face morphing have become increasingly accessible to the general public. It opens up the possibilities for different kinds of face presentation attacks, which can be taken advantage of by impostors to gain unauthorized access of a biometric system. Moreover, the wide availability of 3D printers has caused a shift from print attacks to 3D mask attacks. With increasing types of attacks, it is necessary to come up with a generic and ubiquitous algorithm with a panoptic view of these attacks, and can detect a spoofed image irrespective of the method used. The key contribution of this paper is designing a deep learning based panoptic algorithm for detection of both digital and physical presentation attacks using Cross Asymmetric Loss Function (CALF). The performance is evaluated for digital and physical attacks in three scenarios: ubiquitous environment, individual databases, and cross-attack/cross-database. Experimental results showcase the superior performance of the proposed presentation attack detection algorithm.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"4 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":"133478998","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

A New Approach for EEG-Based Biometric Authentication Using Auditory Stimulation 听觉刺激下基于脑电图的生物识别认证新方法

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987271

Sherif Nagib Abbas Seha, D. Hatzinakos

引用次数: 6

Alignment Free and Distortion Robust Iris Recognition 无对准和失真鲁棒虹膜识别

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987369

Min Ren, Caiyong Wang, Yunlong Wang, Zhenan Sun, T. Tan

{"title":"Alignment Free and Distortion Robust Iris Recognition","authors":"Min Ren, Caiyong Wang, Yunlong Wang, Zhenan Sun, T. Tan","doi":"10.1109/ICB45273.2019.8987369","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987369","url":null,"abstract":"Iris recognition is a reliable personal identification method but there is still much room to improve its accuracy especially in less-constrained situations. For example, free movement of head pose may cause large rotation difference between iris images. And illumination variations may cause irregular distortion of iris texture. To match intra-class iris images with head rotation robustly, the existing solutions usually need a precise alignment operation by exhaustive search within a determined range in iris image preprosessing or brute-force searching the minimum Hamming distance in iris feature matching. In the wild enviroments, iris rotation is of much greater uncertainty than that in constrained situations and exhaustive search within a determined range is impracticable. This paper presents a unified feature-level solution to both alignment free and distortion robust iris recognition in the wild. A new deep learning based method named Alignment Free Iris Network (AFINet) is proposed, which utilizes a trainable VLAD (Vector of Locally Aggregated Descriptors) encoder called NetVLAD [18] to decouple the correlations between local representations and their spatial positions. And deformable convolution [5] is leveraged to overcome iris texture distortion by dense adaptive sampling. The results of extensive experiments on three public iris image databases and the simulated degradation databases show that AFINet significantly outperforms state-of-art iris recognition methods.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"96 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":"126885213","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: 5