2021 IEEE International Joint Conference on Biometrics (IJCB)最新文献_第2页

Vulnerability Assessment and Presentation Attack Detection Using a Set of Distinct Finger Vein Recognition Algorithms 基于一套独特手指静脉识别算法的漏洞评估与表示攻击检测

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484351

Johannes Schuiki, Georg Wimmer, A. Uhl

引用次数: 4

Child Face Age Progression and Regression using Self-Attention Multi-Scale Patch GAN 基于自注意多尺度贴片GAN的儿童面部年龄进展与回归

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484329

Praveen Kumar Chandaliya, N. Nain

{"title":"Child Face Age Progression and Regression using Self-Attention Multi-Scale Patch GAN","authors":"Praveen Kumar Chandaliya, N. Nain","doi":"10.1109/IJCB52358.2021.9484329","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484329","url":null,"abstract":"Face age progression and regression have accumulated significant dynamic research enthusiasm because of its gigantic effect on a wide scope of handy applications including finding lost/wanted persons, cross-age face recognition, amusement, and cosmetic studies. The two primary necessities of face age progression and regression, are identity preservation and aging exactitude. The existing state-of-the-art frameworks mostly focus on adult or long-span aging. In this work, we propose a child face age-progress and regress framework that generates photo-realistic face images with preserved identity.To facilitate child age synthesis, we apply a multi-scale patch discriminator learning strategy for training conditional generative adversarial nets (cGAN) which in-creases the stability of the discriminator, thereby making the learning task progressively more difficult for the generator. Moreover, we also introduce Self-Attention Block (SAB) to learn global and long-term dependencies within an internal representation of a child’s face. Thus, we present coarse-to-fine Self-Attention Multi-Scale Patch generative adversarial nets (SAMSP-GAN) model. Our new objective function, as well as multi-scale patch discrimination and, has shown both qualitative and quantitative improvements over the state-of-the-art approaches in terms of face verification, rank-1 identification, and age estimation on benchmarked children datasets.","PeriodicalId":175984,"journal":{"name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","volume":"98 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":"114552448","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

Leveraging Adversarial Learning for the Detection of Morphing Attacks 利用对抗性学习检测变形攻击

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484383

Zander Blasingame, Chen Liu

{"title":"Leveraging Adversarial Learning for the Detection of Morphing Attacks","authors":"Zander Blasingame, Chen Liu","doi":"10.1109/IJCB52358.2021.9484383","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484383","url":null,"abstract":"An emerging threat towards face recognition systems (FRS) is face morphing attack, which involves the combination of two faces from two different identities into a singular image that would trigger an acceptance for either identity within the FRS. Many of the existing morphing attack detection (MAD) approaches have been trained and evaluated on datasets with limited variation of image characteristics, which can make the approach prone to overfitting. Additionally, there has been difficulty in developing MAD algorithms which can generalize beyond the morphing attack they were trained on, as shown by the most recent NIST FRVT MORPH report. Furthermore, the Single image based MAD (S-MAD) problem has had poor performance, especially when compared to its counterpart, Differential based MAD (D-MAD). In this work, we propose a novel architecture for training deep learning based S-MAD algorithms that leverages adversarial learning to train a more robust detector. The performance of the proposed S-MAD method is benchmarked against the state-of-the-art VGG19 based S-MAD algorithm over 36 experiments using the ISO-IEC 30107-3 evaluation metrics. The proposed method has demonstrated superior and robust detection performance of less than 5% D-EER when evaluated against different morphing attacks.","PeriodicalId":175984,"journal":{"name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","volume":"49 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":"116115421","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

Universal Adversarial Spoofing Attacks against Face Recognition 针对人脸识别的通用对抗性欺骗攻击

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484380

Takuma Amada, Seng Pei Liew, Kazuya Kakizaki, Toshinori Araki

引用次数: 3

Learning Discriminative Speaker Embedding by Improving Aggregation Strategy and Loss Function for Speaker Verification 基于改进聚合策略和损失函数的说话人识别嵌入学习

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484331

Chengfang Luo, Xin Guo, Aiwen Deng, Wei Xu, Junhong Zhao, Wenxiong Kang

{"title":"Learning Discriminative Speaker Embedding by Improving Aggregation Strategy and Loss Function for Speaker Verification","authors":"Chengfang Luo, Xin Guo, Aiwen Deng, Wei Xu, Junhong Zhao, Wenxiong Kang","doi":"10.1109/IJCB52358.2021.9484331","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484331","url":null,"abstract":"The embedding-based speaker verification (SV) technology has witnessed significant progress due to the advances of deep convolutional neural networks (DCNN). However, how to improve the discrimination of speaker embedding in the open world SV task is still the focus of current research in the community. In this paper, we improve the discriminative power of speaker embedding from three-fold: (1) NeXtVLAD is introduced to aggregate frame-level features, which decomposes the high-dimensional frame-level features into a group of low-dimensional vectors before applying VLAD aggregation. (2) A multi-scale aggregation strategy (MSA) assembled with NeXtVLAD is designed with the purpose of fully extract speaker information from the frame-level feature in different hidden layers of DCNN. (3) A mutually complementary assembling loss function is proposed to train the model, which consists of a prototypical loss and a marginal-based softmax loss. Extensive experiments have been conducted on the VoxCeleb-1 dataset, and the experimental results show that our proposed system can obtain significant performance improvements compared with the baseline, and obtains new state-of-the-art results. The source code of this paper is available at https://github.com/LCF2764/Discriminative-Speaker-Embedding.","PeriodicalId":175984,"journal":{"name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","volume":"96 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":"129297274","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

STERLING: Towards Effective ECG Biometric Recognition 斯特林:迈向有效的心电生物识别

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484360

Kuikui Wang, Gongping Yang, Lu Yang, Yuwen Huang, Yilong Yin

引用次数: 2

Static and Dynamic Features Analysis from Human Skeletons for Gait Recognition 用于步态识别的人体骨骼静态和动态特征分析

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484378

Ziqiong Li, Shiqi Yu, Edel B. García Reyes, Caifeng Shan, Yan-Ran Li

引用次数: 4

Estimation of Gait Relative Attribute Distributions using a Differentiable Trade-off Model of Optimal and Uniform Transports 基于最优和均匀运输的可微权衡模型的步态相对属性分布估计

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484362

Yasushi Makihara, Yuta Hayashi, Allam Shehata, D. Muramatsu, Y. Yagi

{"title":"Estimation of Gait Relative Attribute Distributions using a Differentiable Trade-off Model of Optimal and Uniform Transports","authors":"Yasushi Makihara, Yuta Hayashi, Allam Shehata, D. Muramatsu, Y. Yagi","doi":"10.1109/IJCB52358.2021.9484362","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484362","url":null,"abstract":"This paper describes a method for estimating gait relative attribute distributions. Existing datasets for gait relative attributes have only three-grade annotations, which cannot be represented in the form of distributions. Thus, we first create a dataset with seven-grade annotations for five gait relative attributes (i.e., beautiful, graceful, cheerful, imposing, and relaxed). Second, we design a deep neural network to handle gait relative attribute distributions. Although the ground-truth (i.e., annotation) is given in a relative (or pairwise) manner with some degree of uncertainty (i.e., inconsistency among multiple annotators), it is desirable for the system to output an absolute attribute distribution for each gait input. Therefore, we develop a model that converts a pair of absolute attribute distributions into a relative attribute distribution. More specifically, we formulate the conversion as a transportation process from one absolute attribute distribution to the other, then derive a differentiable model that determines the trade-off between optimal transport and uniform transport. Finally, we learn the network parameters by minimizing the dissimilarity between the estimated and ground-truth distributions through the Kullback–Leibler divergence and the expectation dissimilarity. Experimental results show that the proposed method successfully estimates both absolute and relative attribute distributions.","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":"121936734","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

ReSGait: The Real-Scene Gait Dataset res步态:真实场景步态数据集

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484347

Zihao Mu, F. M. Castro, M. Marín-Jiménez, Nicolás Guil Mata, Yan-Ran Li, Shiqi Yu

{"title":"ReSGait: The Real-Scene Gait Dataset","authors":"Zihao Mu, F. M. Castro, M. Marín-Jiménez, Nicolás Guil Mata, Yan-Ran Li, Shiqi Yu","doi":"10.1109/IJCB52358.2021.9484347","DOIUrl":"https://doi.org/10.1109/IJCB52358.2021.9484347","url":null,"abstract":"Many studies have shown that gait recognition can be used to identify humans at a long distance, with promising results on current datasets. However, those datasets are collected under controlled situations and predefined conditions, which limits the extrapolation of the results to unconstrained situations in which the subjects walk freely in scenes. To cover this gap, we release a novel real-scene gait dataset (ReSGait), which is the first dataset collected in unconstrained scenarios with freely moving subjects and not controlled environmental parameters. Overall, our dataset is composed of 172 subjects and 870 video sequences, recorded over 15 months. Video sequences are labeled with gender, clothing, carrying conditions, taken walking route, and whether mobile phones were used or not. Therefore, the main characteristics of our dataset that differentiate it from other datasets are as follows: (i) uncontrolled real-life scenes and (ii) long recording time. Finally, we empirically assess the difficulty of the proposed dataset by evaluating state-of-the-art gait approaches for silhouette and pose modalities. The results reveal an accuracy of less than 35%, showing the inherent level of difficulty of our dataset compared to other current datasets, in which accuracies are higher than 90%. Thus, our proposed dataset establishes a new level of difficulty in the gait recognition problem, much closer to real life.","PeriodicalId":175984,"journal":{"name":"2021 IEEE International Joint Conference on Biometrics (IJCB)","volume":"138 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":"116608787","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

Self-Augmented Heterogeneous Face Recognition 自增强异构人脸识别

2021 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2021-08-04 DOI: 10.1109/IJCB52358.2021.9484335

Zongcai Sun, Chaoyou Fu, Mandi Luo, R. He

引用次数: 3