2020 IEEE International Joint Conference on Biometrics (IJCB)最新文献

Distinctive Feature Representation for Contactless 3D Hand Biometrics using Surface Normal Directions 基于表面法线方向的非接触式3D手部生物识别特征表示

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304860

Kevin H. M. Cheng, Ajay Kumar

引用次数: 2

Leveraging Auxiliary Tasks for Height and Weight Estimation by Multi Task Learning 利用辅助任务进行多任务学习的身高和体重估计

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304855

Dan Han, Jie Zhang, S. Shan

引用次数: 5

3DPC-Net: 3D Point Cloud Network for Face Anti-spoofing 3DPC-Net:人脸防欺骗的三维点云网络

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304873

Xuan Li, Jun Wan, Yi Jin, Ajian Liu, G. Guo, Stan Z. Li

引用次数: 17

Unconstrained Face Identification using Ensembles trained on Clustered Data 基于聚类数据集合体的无约束人脸识别

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304882

R. H. Vareto, W. R. Schwartz

引用次数: 2

SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment SSBC 2020:移动环境下的巩膜分割基准竞争

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304881

M. Vitek, A. Das, Y. Pourcenoux, A. Missler, C. Paumier, S. Das, I. De Ghosh, D. Lucio, L. A. Zanlorensi, D. Menotti, F. Boutros, N. Damer, J. H. Grebe, A. Kuijper, J. Hu, Y. He, C. Wang, H. Liu, Y. Wang, Z. Sun, D. Osorio-Roig, C. Rathgeb, C. Busch, J. Tapia, A. Valenzuela, G. Zampoukis, Lazaros Tsochatzidis, I. Pratikakis, S. Nathan, R. Suganya, V. Mehta, A. Dhall, K. Raja, G. Gupta, J. Khiarak, M. Akbari-Shahper, F. Jaryani, M. Asgari-Chenaghlu, R. Vyas, S. Dakshit, P. Peer, U. Pal, V. Štruc

{"title":"SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment","authors":"M. Vitek, A. Das, Y. Pourcenoux, A. Missler, C. Paumier, S. Das, I. De Ghosh, D. Lucio, L. A. Zanlorensi, D. Menotti, F. Boutros, N. Damer, J. H. Grebe, A. Kuijper, J. Hu, Y. He, C. Wang, H. Liu, Y. Wang, Z. Sun, D. Osorio-Roig, C. Rathgeb, C. Busch, J. Tapia, A. Valenzuela, G. Zampoukis, Lazaros Tsochatzidis, I. Pratikakis, S. Nathan, R. Suganya, V. Mehta, A. Dhall, K. Raja, G. Gupta, J. Khiarak, M. Akbari-Shahper, F. Jaryani, M. Asgari-Chenaghlu, R. Vyas, S. Dakshit, P. Peer, U. Pal, V. Štruc","doi":"10.1109/IJCB48548.2020.9304881","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304881","url":null,"abstract":"The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"87 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":"131903286","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

Pixel Sampling for Style Preserving Face Pose Editing 像素采样的风格保留面部姿势编辑

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304867

Xiangnan Yin, Di Huang, Hongyu Yang, Zehua Fu, Yunhong Wang, Liming Chen

{"title":"Pixel Sampling for Style Preserving Face Pose Editing","authors":"Xiangnan Yin, Di Huang, Hongyu Yang, Zehua Fu, Yunhong Wang, Liming Chen","doi":"10.1109/IJCB48548.2020.9304867","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304867","url":null,"abstract":"The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc. In this paper, we take advantage of the well-known frontal/profile optical illusion and present a novel two-stage approach to solve the aforementioned dilemma, where the task of face pose manipulation is cast into face inpainting. By selectively sampling pixels from the input face and slightly adjust their relative locations with the proposed “Pixel Attention Sampling” module, the face editing result faithfully keeps the identity information as well as the image style unchanged. By leveraging high-dimensional embedding at the inpainting stage, finer details are generated. Further, with the 3D facial landmarks as guidance, our method is able to manipulate face pose in three degrees of freedom, i.e., yaw, pitch, and roll, resulting in more flexible face pose editing than merely controlling the yaw angle as usually achieved by the current state-of-the-art. Both the qualitative and quantitative evaluations validate the superiority of the proposed approach.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"98 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":"132493352","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: 1

An Assessment of GANs for Identity-related Applications 身份相关应用的gan评估

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304879

Richard T. Marriott, Safa Madiouni, S. Romdhani, S. Gentric, Liming Chen

引用次数: 10

A Metric Learning Approach to Eye Movement Biometrics 眼动生物识别的度量学习方法

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304859

D. Lohr, Henry K. Griffith, Samantha Aziz, Oleg V. Komogortsev

引用次数: 11

Is Warping-based Cancellable Biometrics (still) Sensible for Face Recognition? 基于扭曲的可取消生物特征(仍然)适用于人脸识别吗?

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304870

Simon Kirchgasser, A. Uhl, Yoanna Martínez-Díaz, Heydi Mendez Vazquez

引用次数: 8

Cross Modal Person Re-identification with Visual-Textual Queries 视觉文本查询的跨模态人物再识别

2020 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2020-09-28 DOI: 10.1109/IJCB48548.2020.9304940

Ammarah Farooq, Muhammad Awais, J. Kittler, A. Akbari, S. S. Khalid

{"title":"Cross Modal Person Re-identification with Visual-Textual Queries","authors":"Ammarah Farooq, Muhammad Awais, J. Kittler, A. Akbari, S. S. Khalid","doi":"10.1109/IJCB48548.2020.9304940","DOIUrl":"https://doi.org/10.1109/IJCB48548.2020.9304940","url":null,"abstract":"Classical person re-identification approaches assume that a person of interest has appeared across different cameras and can be queried by one of the existing images. However, in real-world surveillance scenarios, frequently no visual information will be available about the queried person. In such scenarios, a natural language description of the person by a witness will provide the only source of information for retrieval. In this work, person re-identification using both vision and language information is addressed under all possible gallery and query scenarios. A two stream deep convolutional neural network framework supervised by identity based cross entropy loss is presented. Canonical Correlation Analysis is performed to enhance the correlation between the two modalities in a joint latent embedding space. To investigate the benefits of the proposed approach, a new testing protocol under a multi modal ReID setting is proposed for the test split of the CUHK-PEDES and CUHK-SYSU benchmarks. The experimental results verify that the learnt visual representations are more robust and perform 20% better during retrieval as compared to a single modality system.","PeriodicalId":417270,"journal":{"name":"2020 IEEE International Joint Conference on Biometrics (IJCB)","volume":"7 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":"130655285","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