2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)最新文献_第5页

CrazyFaces: Unassisted Circumvention of Watchlist Face Identification CrazyFaces:监视名单面部识别的无辅助规避

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698557

N. Damer, Yaza Wainakh, Viola Boller, Sven von den Berken, Philipp Terhörst, Andreas Braun, Arjan Kuijper

{"title":"CrazyFaces: Unassisted Circumvention of Watchlist Face Identification","authors":"N. Damer, Yaza Wainakh, Viola Boller, Sven von den Berken, Philipp Terhörst, Andreas Braun, Arjan Kuijper","doi":"10.1109/BTAS.2018.8698557","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698557","url":null,"abstract":"Once upon a time, there was a blacklisted criminal who usually avoided appearing in public. He was surfing the Web, when he noticed, what had to be a targeted advertisement announcing a concert of his favorite band. The concert was in a near town, and the only way to get there was by train. He was worried, because he heard in the news about the new face identification system installed at the train station. From his last stay with the police, he remembers that they took these special face images with the white background. He thought about what can he do to avoid being identified and an idea popped in his mind “what if I can make a crazy-face, as the kids call it, to make my face look different? What do I exactly have to do? And will it work?”. He called his childhood geeky friend and asked him if he can build him a face recognition application he can tinker with. The geeky friend was always interested in such small projects where he can use open-source resources and didn’t really care about the goal, as usual. The criminal tested the application and played around, trying to figure out how can he make a crazy-face that won’t be identified as himself. On the day of the concert, he took off to the train station with some doubt in his mind and fear in his soul. To know what happened next, you should read the rest of this paper.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123324437","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: 19

A Non-Contact PPG Biometric System Based on Deep Neural Network 基于深度神经网络的非接触式PPG生物识别系统

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698552

Omkar R. Patil, Wen Wang, Yang Gao, Wenyao Xu, Zhanpeng Jin

引用次数: 14

SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition SmartBox:人脸识别对抗性检测和缓解算法的基准测试

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698567

Akhil Goel, Anirudh Singh, Akshay Agarwal, Mayank Vatsa, Richa Singh

{"title":"SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition","authors":"Akhil Goel, Anirudh Singh, Akshay Agarwal, Mayank Vatsa, Richa Singh","doi":"10.1109/BTAS.2018.8698567","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698567","url":null,"abstract":"Deep learning models are widely used for various purposes such as face recognition and speech recognition. However, researchers have shown that these models are vulnerable to adversarial attacks. These attacks compute perturbations to generate images that decrease the performance of deep learning models. In this research, we have developed a toolbox, termed as SmartBox, for benchmarking the performance of adversarial attack detection and mitigation algorithms against face recognition. SmartBox is a python based toolbox which provides an open source implementation of adversarial detection and mitigation algorithms. In this research, Extended Yale Face Database B has been used for generating adversarial examples using various attack algorithms such as DeepFool, Gradient methods, Elastic-Net, and $L_{2}$ attack. SmartBox provides a platform to evaluate newer attacks, detection models, and mitigation approaches on a common face recognition benchmark. To assist the research community, the code of SmartBox is made available11http://iab-rubric.org/resources/SmartBox.html.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125892922","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

Unknown Presentation Attack Detection with Face RGB Images 未知表示攻击检测与人脸RGB图像

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698574

Fei Xiong, Wael AbdAlmageed

{"title":"Unknown Presentation Attack Detection with Face RGB Images","authors":"Fei Xiong, Wael AbdAlmageed","doi":"10.1109/BTAS.2018.8698574","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698574","url":null,"abstract":"The vulnerability of biometric identification systems to presentation attacks, also a.k.a. spoof attacks, has received great attention from the biometrics community. Face presentation attacks are particularly easy to fabricate, because face image and videos are easy to obtain from social media, and most face recognition systems rely on 2D RGB image sensors only. To our best knowledge, all the existing face Presentation Attack Detection (PAD) research works focus on solving the problem in a close-set setup, even though it is an open-set problem in real life where Unknown Presentation Attack Detection (UPAD) algorithms are demanded. In this paper, One-Class SVM and AutoEncoder based outliers detection algorithms, are proposed to solve the UPAD problem. It aims to detect the attack presentations from different presentation attack instrument species that are not included in the training and validation set. Both algorithms, together with the state of the art Local Binary Pattern feature are evaluated and compared on four largest face PAD datasets, including the latest Oulu dataset - the largest face PAD dataset so far. Moreover, four UPAD protocols are proposed to evaluate the robustness of the algorithms against environment and device variations. These comprehensive evaluations show that the proposed methods are more accurate on detecting unknown presentation attacks than the state of the art PAD algorithms under complex environment and capturing device variations.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"22 6S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115948340","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: 48

Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect? 人脸识别的图像不可知论普遍对抗性扰动难以检测吗?

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698548

Akshay Agarwal, Richa Singh, Mayank Vatsa, N. Ratha

{"title":"Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect?","authors":"Akshay Agarwal, Richa Singh, Mayank Vatsa, N. Ratha","doi":"10.1109/BTAS.2018.8698548","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698548","url":null,"abstract":"High performance of deep neural network based systems have attracted many applications in object recognition and face recognition. However, researchers have also demonstrated them to be highly sensitive to adversarial perturbation and hence, tend to be unreliable and lack robustness. While most of the research on adversarial perturbation focuses on image specific attacks, recently, image-agnostic Universal perturbations are proposed which learn the adversarial pattern over training distribution and have broader impact on real-world security applications. Such adversarial attacks can have compounding effect on face recognition where these visually imperceptible attacks can cause mismatches. To defend against adversarial attacks, sophisticated detection approaches are prevalent but most of the existing approaches do not focus on image-agnostic attacks. In this paper, we present a simple but efficient approach based on pixel values and Principal Component Analysis as features coupled with a Support Vector Machine as the classifier, to detect image-agnostic universal perturbations. We also present evaluation metrics, namely adversarial perturbation class classification error rate, original class classification error rate, and average classification error rate, to estimate the performance of adversarial perturbation detection algorithms. The experimental results on multiple databases and different DNN architectures show that it is indeed not required to build complex detection algorithms; rather simpler approaches can yield higher detection rates and lower error rates for image agnostic adversarial perturbation.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"452 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116125868","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: 48

Adversarial Embedding and Variational Aggregation for Video Face Recognition 视频人脸识别中的对抗嵌入和变分聚合

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698560

Yibo Hu, Lingxiao Song, Ting Yu, R. He, Zhenan Sun

{"title":"Adversarial Embedding and Variational Aggregation for Video Face Recognition","authors":"Yibo Hu, Lingxiao Song, Ting Yu, R. He, Zhenan Sun","doi":"10.1109/BTAS.2018.8698560","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698560","url":null,"abstract":"Video face recognition is a challenging recognition problem due to low-quality and redundant video data. In order to efficiently address the problem, this paper proposes an adversarial embedding and variational aggregation (AEVA) approach that takes discriminative information from adversarial learning to implicitly constrain the distributions of video features. AEVA contains two parts: adversarial embedding learning and variational aggregation learning. The former contributes to discriminative feature embedding space by a self-adversarial process. It reduces intra-class distance of the same subject in an adversarial way. The latter aims to improve aggregated video representation by forcing latent feature distribution to be as close to real feature distribution as possible. An attentional weighting network and a variational inference structure are used to aggregate the features from one video and generate the latent features. Both parts have neither complex sampling strategies nor the hyperparameter settings from deep metric learning. Our approach achieves state-of-the-art performance for video face recognition on four widely used benchmarks, including YouTubeFace, IJB-A, YouTube Celebrities and Celebrity-1000.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132395466","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: 0

From the Perspective of CNN to Adversarial Iris Images 从CNN到对抗性虹膜图像的视角

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698562

Yi Huang, A. Kong, Kwok-Yan Lam

{"title":"From the Perspective of CNN to Adversarial Iris Images","authors":"Yi Huang, A. Kong, Kwok-Yan Lam","doi":"10.1109/BTAS.2018.8698562","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698562","url":null,"abstract":"IrisCode, the most influential iris recognition algorithm, has been extensively applied in national identity programmes and border controls. Currently over one billion people spanning approximately 170 nationalities have been enrolled in IrisCode. It is vital to thoroughly study this crucial algorithm, especially for issues related to its security. How to generate iris images from its templates is one of such security issues. The existing generation methods can produce images from iris templates and the synthetic images can be used to match other real iris images. However, their quality is very low with obvious artifacts. Considering the feature extraction process of IrisCode as a non-differentiable shallow convolutional neural network and using a differentiable function to approximate its step function, the generation process can be formulated as an unconstrained minimization problem. It can be mathematically proven that the solution of this formulation is the same as that of the convex polyhedral cone method proposed before. By exploiting the unconstrained formulation and the step function directly, a constrained convex minimization formulation with an inputted iris image selected by a search function as an information carrier is derived. The experimental results on the UBIRIS.v1 database and the WVU database demonstrate that the proposed algorithm can generate high quality iris images and significantly outperform the previous methods in terms of visual quality and six image quality metrics. They can also be matched with other real iris images in the two databases.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126525486","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: 4

A Likelihood Ratio Classifier for Histogram Features 直方图特征的似然比分类器

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-09-21 DOI: 10.1109/BTAS.2018.8698573

R. Veldhuis, K. Raja, Raghavendra Ramachandra

引用次数: 0

Thermal Features for Presentation Attack Detection in Hand Biometrics 手部生物识别中呈现攻击检测的热特征

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-09-12 DOI: 10.1109/BTAS.2018.8698546

Ewelina Bartuzi, Mateusz Trokielewicz

{"title":"Thermal Features for Presentation Attack Detection in Hand Biometrics","authors":"Ewelina Bartuzi, Mateusz Trokielewicz","doi":"10.1109/BTAS.2018.8698546","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698546","url":null,"abstract":"This paper proposes a method for utilizing thermal features of the hand for the purpose of presentation attack detection (PAD) that can be employed in a hand biometrics system’s pipeline. By envisaging two different operational modes of our system, and by employing a DCNN-based classifiers fine-tuned with a dataset of real and fake hand representations captured in both visible and thermal spectrum, we were able to bring two important deliverables. First, a PAD method operating in an open-set mode, capable of correctly discerning 100% of fake thermal samples, achieving Attack Presentation Classification Error Rate (APCER) and Bona-Fide Presentation Classification Error Rate (BPCER) equal to 0%, which can be easily implemented into any existing system as a separate component. Second, a hand biometrics system operating in a closed-set mode, that has PAD built right into the recognition pipeline, and operating simultaneously with the user-wise classification, achieving rank-1 recognition accuracy of up to 99.75%. We also show that thermal images of the human hand, in addition to liveness features they carry, can also improve classification accuracy of a biometric system, when coupled with visible light images. To follow the reproducibility guidelines and to stimulate further research in this area, we share the trained model weights, source codes, and a newly created dataset of fake hand representations with interested researchers.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128351733","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: 4

Learning A Shared Transform Model for Skull to Digital Face Image Matching 颅骨与数字人脸图像匹配的共享变换模型研究

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-08-14 DOI: 10.1109/BTAS.2018.8698604

Maneet Singh, Shruti Nagpal, Richa Singh, Mayank Vatsa, A. Noore

引用次数: 4