2021 IEEE International Joint Conference on Biometrics (IJCB)最新文献_第3页

Deep Multi-loss Hashing Network for Palmprint Retrieval and Recognition 基于深度多损失哈希网络的掌纹检索与识别

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484403

Wei Jia, Shuwei Huang, Bin Wang, Lunke Fei, Yang Zhao, Hai Min

引用次数: 7

A Temporal Memory-based Continuous Authentication System 基于时间内存的连续认证系统

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484365

S. Gopal, Diksha Shukla

{"title":"A Temporal Memory-based Continuous Authentication System","authors":"S. Gopal, Diksha Shukla","doi":"10.1109/IJCB52358.2021.9484365","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484365","url":null,"abstract":"With the emerging use of technology, verifying a user’s identity continuously throughout a device’s usage has become increasingly important. This paper proposes an authentication system that unobtrusively verifies a user’s identity continuously, based on his/her hand movement patterns captured using accelerometer, while a user performs free-text typing. Our model validates a user’s identity with a verification decision in every ≈ 20ms interval. The authentication model utilizes a short temporal memory of size M of a user’s hand movement patterns. Experiments on different values of M suggests that the model shows an improved and consistent performance by increasing the size of the temporal memory of a user’s hand movement patterns to M ≈ 300ms.The authentication system requires only a user’s hand movement signals in order to authenticate a user on a device. Experiments on the hand movement patterns of 27 volunteer participants, captured using motion sensors of a Sony Smartwatch while they performed free-text typing on a desktop/laptop device, show that our model could achieve an average authentication accuracy of 99.8% with an average False Accept Rate (FAR) of 0.0003 and an average False Reject Rate (FRR) of 0.0034.","PeriodicalId":175984,"journal":{"name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134202131","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

RamFace: Race Adaptive Margin Based Face Recognition for Racial Bias Mitigation RamFace:基于种族自适应边缘的种族偏见缓解人脸识别

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484352

Zhanjia Yang, Xiangping Zhu, Changyuan Jiang, Wenshuang Liu, Linlin Shen

{"title":"RamFace: Race Adaptive Margin Based Face Recognition for Racial Bias Mitigation","authors":"Zhanjia Yang, Xiangping Zhu, Changyuan Jiang, Wenshuang Liu, Linlin Shen","doi":"10.1109/IJCB52358.2021.9484352","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484352","url":null,"abstract":"Recent studies show that there exist significant racial bias among state-of-the-art (SOTA) face recognition algorithms, i.e., the accuracy for Caucasian is consistently higher than that for other races like African and Asian. To mitigate racial bias, we propose the race adaptive margin based face recognition (RamFace) model, designed under the multi-task learning framework with the race classification as the auxiliary task. The experiments show that the race classification task can enforce the model to learn the racial features and thus improve the discriminability of the extracted feature representations. In addition, a racial bias robust loss function, i.e., race adaptive margin loss, is proposed such that different optimal margins can be automatically derived for different races in training the model, which further mitigates the racial bias. The experimental results show that on RFW dataset, our model not only achieves SOTA face recognition accuracy but also mitigates the racial bias problem. Besides, RamFace is also tested on several public face recognition evaluation benchmarks, i.e., LFW, CPLFW and CALFW, and achieves better performance than the commonly used face recognition methods, which justifies the generalization capability of RamFace.","PeriodicalId":175984,"journal":{"name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133507571","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: 14

Feasibility of Morphing-Attacks in Vascular Biometrics 血管生物识别中变形攻击的可行性

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484372

Altan K. Aydemir, Jutta Hämmerle-Uhl, A. Uhl

引用次数: 0

PointFace: Point Set Based Feature Learning for 3D Face Recognition PointFace:基于点集的三维人脸识别特征学习

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484368

Changyuan Jiang, Shisong Lin, Wei Chen, Feng Liu, Linlin Shen

引用次数: 8

Message from General Chairs of IJCB 2021 2021年国际jcb大会主席致辞

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/ijcb52358.2021.9521650

引用次数: 0

High Quality Facial Data Synthesis and Fusion for 3D Low-quality Face Recognition 面向3D低质量人脸识别的高质量人脸数据合成与融合

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484339

Shisong Lin, Changyuan Jiang, Feng Liu, Linlin Shen

引用次数: 6

Robust End-to-End Hand Identification via Holistic Multi-Unit Knuckle Recognition 基于整体多单元关节识别的鲁棒端到端手部识别

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484356

Ritesh Vyas, Hossein Rahmani, Ricki Boswell-Challand, P. Angelov, Sue Black, Bryan M. Williams

{"title":"Robust End-to-End Hand Identification via Holistic Multi-Unit Knuckle Recognition","authors":"Ritesh Vyas, Hossein Rahmani, Ricki Boswell-Challand, P. Angelov, Sue Black, Bryan M. Williams","doi":"10.1109/IJCB52358.2021.9484356","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484356","url":null,"abstract":"In many cases of serious crime, images of a hand can be the only evidence available for the forensic identification of the offender. As well as placing them at the scene, such images and video evidence offer proof of the offender committing the crime. The knuckle creases of the human hand have emerged as an effective biometric trait and been used to identify the perpetrators of child abuse in forensic investigations. However, manual utilization of knuckle creases for identification is highly time consuming and can be subjective, requiring the expertise of experienced forensic anthropologists whose availability is very limited. Hence, there arises a need for an automated approach for localization and comparison of knuckle patterns. In this paper, we present a fully automatic end-to-end approach which localizes the minor, major and base knuckles in images of the hand, and effectively uses them for identification achieving state-of-the-art results. This work improves on existing approaches and allows us to strengthen cases further by objectively combining multiple knuckles and knuckle types to obtain a holistic matching result for comparing two hands. This yields a stronger and more robust multi-unit biometric and facilitates the large-scale examination of the potential of knuckle-based identification. Evaluated on two large landmark datasets, the proposed framework achieves equal error rates (EER) of 1.0-1.9%, rank-1 accuracies of 99.3-100% and decidability indices of 5.04-5.83. We make the full results available via a novel online GUI to raise awareness with the general public and forensic investigators about the identifiability of various knuckle regions. These strong results demonstrate the value of our holistic approach to hand identification from knuckle patterns and their utility in forensic investigations.","PeriodicalId":175984,"journal":{"name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692319","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

Sequential Interactive Biased Network for Context-Aware Emotion Recognition 上下文感知情感识别的顺序交互偏见网络

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484370

Xinpeng Li, Xiaojiang Peng, Changxing Ding

引用次数: 2

Contrastive Uncertainty Learning for Iris Recognition with Insufficient Labeled Samples 标记样本不足情况下虹膜识别的对比不确定性学习

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484388

Jianze Wei, R. He, Zhenan Sun

{"title":"Contrastive Uncertainty Learning for Iris Recognition with Insufficient Labeled Samples","authors":"Jianze Wei, R. He, Zhenan Sun","doi":"10.1109/IJCB52358.2021.9484388","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484388","url":null,"abstract":"Cross-database recognition is still an unavoidable challenge when deploying an iris recognition system to a new environment. In the paper, we present a compromise problem that resembles the real-world scenario, named iris recognition with insufficient labeled samples. This new problem aims to improve the recognition performance by utilizing partially-or un-labeled data. To address the problem, we propose Contrastive Uncertainty Learning (CUL) by integrating the merits of uncertainty learning and contrastive self-supervised learning. CUL makes two efforts to learn a discriminative and robust feature representation. On the one hand, CUL explores the uncertain acquisition factors and adopts a probabilistic embedding to represent the iris image. In the probabilistic representation, the identity information and acquisition factors are disentangled into the mean and variance, avoiding the impact of uncertain acquisition factors on the identity information. On the other hand, CUL utilizes probabilistic embeddings to generate virtual positive and negative pairs. Then CUL builds its contrastive loss to group the similar samples closely and push the dissimilar samples apart. The experimental results demonstrate the effectiveness of the proposed CUL for iris recognition with insufficient labeled samples.","PeriodicalId":175984,"journal":{"name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121679513","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