2017 IEEE International Joint Conference on Biometrics (IJCB)最新文献_第10页

Periocular recognition in cross-spectral scenario 交叉光谱场景下的眼周识别

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-06-29 DOI: 10.1109/BTAS.2017.8272757

S. S. Behera, Mahesh Gour, Vivek Kanhangad, N. Puhan

引用次数: 16

You are how you walk: Uncooperative MoCap gait identification for video surveillance with incomplete and noisy data 你就是你走路的样子:不合作的动作捕捉步态识别，用于视频监控不完整和嘈杂的数据

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-05-10 DOI: 10.1109/BTAS.2017.8272700

Michal Balazia, Petr Sojka

引用次数: 9

Deep person re-identification with improved embedding and efficient training 通过改进的嵌入和有效的培训进行深度人员再识别

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-05-09 DOI: 10.1109/BTAS.2017.8272706

Haibo Jin, Xiaobo Wang, Shengcai Liao, S. Li

{"title":"Deep person re-identification with improved embedding and efficient training","authors":"Haibo Jin, Xiaobo Wang, Shengcai Liao, S. Li","doi":"10.1109/BTAS.2017.8272706","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272706","url":null,"abstract":"Person re-identification task has been greatly boosted by deep convolutional neural networks (CNNs) in recent years. The core of which is to enlarge the inter-class distinction as well as reduce the intra-class variance. However, to achieve this, existing deep models prefer to adopt image pairs or triplets to form verification loss, which is inefficient and unstable since the number of training pairs or triplets grows rapidly as the number of training data grows. Moreover, their performance is limited since they ignore the fact that different dimension of embedding may play different importance. In this paper, we propose to employ identification loss with center loss to train a deep model for person re-identification. The training process is efficient since it does not require image pairs or triplets for training while the inter-class distinction and intra-class variance are well handled. To boost the performance, a new feature reweighting (FRW) layer is designed to explicitly emphasize the importance of each embedding dimension, thus leading to an improved embedding. Experiments 1 on several benchmark datasets have shown the superiority of our method over the state-of-the-art alternatives on both accuracy and speed.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131063068","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: 43

Generative convolutional networks for latent fingerprint reconstruction 基于生成卷积网络的潜在指纹重建

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-05-04 DOI: 10.1109/BTAS.2017.8272727

Jan Svoboda, Federico Monti, M. Bronstein

引用次数: 29

SREFI: Synthesis of realistic example face images SREFI:真实示例人脸图像的合成

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-04-21 DOI: 10.1109/BTAS.2017.8272680

Sandipan Banerjee, John S. Bernhard, W. Scheirer, K. Bowyer, P. Flynn

引用次数: 28

Deep 3D face identification 深度三维人脸识别

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-03-30 DOI: 10.1109/BTAS.2017.8272691

Donghyun Kim, Matthias Hernandez, Jongmoo Choi, G. Medioni

引用次数: 110

LOTS about attacking deep features 很多关于攻击深层特征的内容

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2016-11-18 DOI: 10.1109/BTAS.2017.8272695

Andras Rozsa, Manuel Günther, T. Boult

{"title":"LOTS about attacking deep features","authors":"Andras Rozsa, Manuel Günther, T. Boult","doi":"10.1109/BTAS.2017.8272695","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272695","url":null,"abstract":"Deep neural networks provide state-of-the-art performance on various tasks and are, therefore, widely used in real world applications. DNNs are becoming frequently utilized in biometrics for extracting deep features, which can be used in recognition systems for enrolling and recognizing new individuals. It was revealed that deep neural networks suffer from a fundamental problem, namely, they can unexpectedly misclassify examples formed by slightly perturbing correctly recognized inputs. Various approaches have been developed for generating these so-called adversarial examples, but they aim at attacking end-to-end networks. For biometrics, it is natural to ask whether systems using deep features are immune to or, at least, more resilient to attacks than end-to-end networks. In this paper, we introduce a general technique called the layerwise origin-target synthesis (LOTS) that can be efficiently used to form adversarial examples that mimic the deep features of the target. We analyze and compare the adversarial robustness of the end-to-end VGG Face network with systems that use Euclidean or cosine distance between gallery templates and extracted deep features. We demonstrate that iterative LOTS is very effective and show that systems utilizing deep features are easier to attack than the end-to-end network.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131471680","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: 35

AFFACT: Alignment-free facial attribute classification technique 影响:无对齐面部属性分类技术

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2016-11-18 DOI: 10.1109/BTAS.2017.8272686

Manuel Günther, Andras Rozsa, T. Boult

{"title":"AFFACT: Alignment-free facial attribute classification technique","authors":"Manuel Günther, Andras Rozsa, T. Boult","doi":"10.1109/BTAS.2017.8272686","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272686","url":null,"abstract":"Facial attributes are soft-biometrics that allow limiting the search space, e.g., by rejecting identities with non-matching facial characteristics such as nose sizes or eyebrow shapes. In this paper, we investigate how the latest versions of deep convolutional neural networks, ResNets, perform on the facial attribute classification task. We test two loss functions: the sigmoid cross-entropy loss and the Euclidean loss, and find that for classification performance there is little difference between these two. Using an ensemble of three ResNets, we obtain the new state-of-the-art facial attribute classification error of 8.00 % on the aligned images of the CelebA dataset. More significantly, we introduce the Alignment-Free Facial Attribute Classification Technique (AFFACT), a data augmentation technique that allows a network to classify facial attributes without requiring alignment beyond detected face bounding boxes. To our best knowledge, we are the first to report similar accuracy when using only the detected bounding boxes — rather than requiring alignment based on automatically detected facial landmarks — and who can improve classification accuracy with rotating and scaling test images. We show that this approach outperforms the CelebA baseline on unaligned images with a relative improvement of 36.8 %.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129603558","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: 57

UMDFaces: An annotated face dataset for training deep networks UMDFaces:用于训练深度网络的带注释的人脸数据集

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2016-11-04 DOI: 10.1109/BTAS.2017.8272731

Ankan Bansal, Anirudh Nanduri, C. Castillo, Rajeev Ranjan, R. Chellappa

{"title":"UMDFaces: An annotated face dataset for training deep networks","authors":"Ankan Bansal, Anirudh Nanduri, C. Castillo, Rajeev Ranjan, R. Chellappa","doi":"10.1109/BTAS.2017.8272731","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272731","url":null,"abstract":"Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. However, most of the large datasets are maintained by private companies and are not publicly available. The academic computer vision community needs larger and more varied datasets to make further progress. In this paper, we introduce a new face dataset, called UMDFaces, which has 367,888 annotated faces of 8,277 subjects. We also introduce a new face recognition evaluation protocol which will help advance the state-of-the-art in this area. We discuss how a large dataset can be collected and annotated using human annotators and deep networks. We provide human curated bounding boxes for faces. We also provide estimated pose (roll, pitch and yaw), locations of twenty-one key-points and gender information generated by a pre-trained neural network. In addition, the quality of keypoint annotations has been verified by humans for about 115,000 images. Finally, we compare the quality of the dataset with other publicly available face datasets at similar scales.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124701441","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: 191

Latent fingerprint minutia extraction using fully convolutional network 基于全卷积网络的潜在指纹细节提取

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2016-09-30 DOI: 10.1109/BTAS.2017.8272689

Yao Tang, Fei Gao, Jufu Feng

引用次数: 34