2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)最新文献_第6页

Deep Sketch-Photo Face Recognition Assisted by Facial Attributes 基于人脸属性的深度素描-照片人脸识别

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-07-31 DOI: 10.1109/BTAS.2018.8698564

S. M. Iranmanesh, Hadi Kazemi, Sobhan Soleymani, Ali Dabouei, N. Nasrabadi

{"title":"Deep Sketch-Photo Face Recognition Assisted by Facial Attributes","authors":"S. M. Iranmanesh, Hadi Kazemi, Sobhan Soleymani, Ali Dabouei, N. Nasrabadi","doi":"10.1109/BTAS.2018.8698564","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698564","url":null,"abstract":"In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential information about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a coupled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Convolutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common embedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with different facial attributes, while the verification task reduces the intra-personal variations by pulling together all the features that are related to one person. The learned discriminative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary facial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state-of-the-art models in sketch-photo recognition algorithms.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122611773","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

Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers 性别隐私:用于混淆任意性别分类器的半对抗网络集合

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-07-31 DOI: 10.1109/BTAS.2018.8698605

Vahid Mirjalili, S. Raschka, A. Ross

{"title":"Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers","authors":"Vahid Mirjalili, S. Raschka, A. Ross","doi":"10.1109/BTAS.2018.8698605","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698605","url":null,"abstract":"Recent research has proposed the use of Semi Adversarial Networks (SAN) for imparting privacy to face images. SANs are convolutional autoencoders that perturb face images such that the perturbed images cannot be reliably used by an attribute classifier (e.g., a gender classifier) but can still be used by a face matcher for matching purposes. However, the generalizability of SANs across multiple arbitrary gender classifiers has not been demonstrated in the literature. In this work, we tackle the generalization issue by designing an ensemble SAN model that generates a diverse set of perturbed outputs for a given input face image. This is accomplished by enforcing diversity among the individual models in the ensemble through the use of different data augmentation techniques. The goal is to ensure that at least one of the perturbed output faces will confound an arbitrary, previously unseen gender classifier. Extensive experiments using different unseen gender classifiers and face matchers are performed to demonstrate the efficacy of the proposed paradigm in imparting gender privacy to face images.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127584135","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: 41

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction 部分隐指纹重建的ID保留生成对抗网络

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-07-31 DOI: 10.1109/BTAS.2018.8698580

Ali Dabouei, Sobhan Soleymani, Hadi Kazemi, S. M. Iranmanesh, J. Dawson, N. Nasrabadi

{"title":"ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction","authors":"Ali Dabouei, Sobhan Soleymani, Hadi Kazemi, S. M. Iranmanesh, J. Dawson, N. Nasrabadi","doi":"10.1109/BTAS.2018.8698580","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698580","url":null,"abstract":"Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latentfingerprint reconstruction model based on conditional generative adversarial networks $(cGANs)$.Two modifications are applied to the cGANto adapt itfor the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information are considered in the generation process, and prevent the model from filling large missing areas and generating erroneous minutiae. Second, a perceptual IDpreservation approach is developed to force the generator to preserve the IDinformation during the reconstruction process. Using a synthetically generated database of latent fingerprints, the deep network learns to predict missing information from the input latent samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms on several publicly available latent fingerprint datasets. Weachieved rank-10 accuracy of 88.02% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50accuracy of 70.89% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching. Experimental results of matching reconstructed samples in both latent-to-sensor and latent-to-latent frameworks indicate that the proposed method significantly increases the matching accuracy of the fingerprint recognition systems for the latent samples.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121458844","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: 20

Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification 韵律增强的Siamese卷积神经网络跨设备文本无关说话者验证

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-07-31 DOI: 10.1109/BTAS.2018.8698585

Sobhan Soleymani, Ali Dabouei, S. M. Iranmanesh, Hadi Kazemi, J. Dawson, N. Nasrabadi

{"title":"Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification","authors":"Sobhan Soleymani, Ali Dabouei, S. M. Iranmanesh, Hadi Kazemi, J. Dawson, N. Nasrabadi","doi":"10.1109/BTAS.2018.8698585","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698585","url":null,"abstract":"In this paper a novel cross-device text-independent speaker verification architecture is proposed. Majority of the state-of-the-art deep architectures that are used for speaker verification tasks consider Mel-frequency cepstral coefficients. In contrast, our proposed Siamese convolutional neural network architecture uses Mel-frequency spectrogram coefficients to benefit from the dependency of the adjacent spectro-temporal features. Moreover, although spectro-temporal features have proved to be highly reliable in speaker verification models, they only represent some aspects of short-term acoustic level traits of the speaker’s voice. However, the human voice consists of several linguistic levels such as acoustic, lexicon, prosody, and phonetics, that can be utilized in speaker verification models. To compensate for these inherited shortcomings in spectro-temporal features, we propose to enhance the proposed Siamese convolutional neural network architecture by deploying a multilayer perceptron network to incorporate the prosodic, jitter, and shimmer features. The proposed end-to-end verification architecture performs feature extraction and verification simultaneously. This proposed architecture displays significant improvement over classical signal processing approaches and deep algorithms for forensic cross-device speaker verification.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125736358","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

Presentation Attack Detection for Cadaver Iris 尸体虹膜的表示攻击检测

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-07-11 DOI: 10.1109/BTAS.2018.8698542

Mateusz Trokielewicz, A. Czajka, P. Maciejewicz

{"title":"Presentation Attack Detection for Cadaver Iris","authors":"Mateusz Trokielewicz, A. Czajka, P. Maciejewicz","doi":"10.1109/BTAS.2018.8698542","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698542","url":null,"abstract":"This paper presents a deep-learning-based method for iris presentation attack detection (PAD) when iris images are obtained from deceased people. Post-mortem iris recognition, despite being a potentially useful method that could aid forensic identification, can also pose challenges when used inappropriately, i.e. utilizing a dead organ of a person in an unauthorized way. Our approach is based on the VGG-16 architecture fine-tuned with a database of 574 post-mortem, near-infrared iris images from the WarsawBioBase-PostMortem-Iris-v1 database, complemented by a dataset of 256 images of live irises, collected within the scope of this study. Experiments described in this paper show that our approach is able to correctly classify iris images as either representing a live or a dead eye in almost 99% of the trials, averaged over 20 subject-disjoint, train/test splits. We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0%@BPCER≈1% (Attack Presentation and Bona Fide Presentation Classification Error Rates, respectively) when only post-mortem samples collected at least 16 hours post-mortem are considered. Since acquisitions of ante- and post-mortem samples differ significantly, we applied countermeasures to minimize bias in our classification methodology caused by image properties that are not related to the PAD. This included using the same iris sensor in collection of ante- and post-mortem samples, and analysis of class activation maps to ensure that discriminant iris regions utilized by our classifier are related to properties of the eye, and not to those of the acquisition protocol. This paper offers the first known to us PAD method in a postmortem setting, together with an explanation of the decisions made by the convolutional neural network. Along with the paper we offer source codes, weights of the trained network, and a dataset of live iris images to facilitate reproducibility and further research.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123781091","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: 23

Focussing the Beam - A New Laser Illumination Based Data Set Providing Insights to Finger-Vein Recognition 聚焦光束——一种新的基于激光照明的数据集，为手指静脉识别提供见解

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-07-02 DOI: 10.1109/BTAS.2018.8698588

Christof Kauba, B. Prommegger, A. Uhl

{"title":"Focussing the Beam - A New Laser Illumination Based Data Set Providing Insights to Finger-Vein Recognition","authors":"Christof Kauba, B. Prommegger, A. Uhl","doi":"10.1109/BTAS.2018.8698588","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698588","url":null,"abstract":"The vascular pattern inside human fingers has become an emerging biometric trait during the last years, commonly denoted as finger vein recognition. However, the number of publicly available data sets is limited. In order to capture a finger vein data set, a suitable scanner device is needed. The design of such a scanner device is crucial if it comes to image quality, robustness against external influences during the capturing process and consequently to a good recognition performance. In this paper we propose two novel, modular designed, multi-purpose finger vein scanners, both able to capture three fingers at once, together with a publicly available finger-vein data set captured with these scanners. One scanner uses common near-infrared LEDs as a light source. The second one is based on a new concept using near-infrared lasers. Near-infrared lasers are not common in finger-vein recognition before despite their advantages especially in touchless operation. Our recognition performance evaluation confirm the good recognition performance that can be achieved using our proposed scanner design and provides some new insights by conducting sex and age-group specific analysis.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127053750","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: 29

Mobile NIR Iris Recognition: Identifying Problems and Solutions 移动近红外虹膜识别:识别问题和解决方案

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-07-02 DOI: 10.1109/BTAS.2018.8698590

H. Hofbauer, Ehsaneddin Jalilian, Ana F. Sequeira, J. Ferryman, A. Uhl

{"title":"Mobile NIR Iris Recognition: Identifying Problems and Solutions","authors":"H. Hofbauer, Ehsaneddin Jalilian, Ana F. Sequeira, J. Ferryman, A. Uhl","doi":"10.1109/BTAS.2018.8698590","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698590","url":null,"abstract":"The spread of biometric applications in mobile devices handled by untrained users opened the door to sources of noise in mobile iris recognition such as larger extent of rotation in the capture and more off-angle imagery not found so extensively in more constrained acquisition settings. As a result of the limitations of the methods in handling such large degrees of freedom there is often an increase in segmentation errors. In this work, a new near-infrared iris dataset captured with a mobile device is evaluated to analyse, in particular, the rotation observed in images and its impact on segmentation and biometric recognition accuracy. For this study a (manually annotated) ground truth segmentation was used which will be published in tandem with the paper. Similarly to most research challenges in biometrics and computer vision in general, deep learning techniques are proving to outperform classical methods in segmentation methods. The utilization of parameterized CNN-based iris segmentations in biometric recognition is a new but promising field. The results presented show how this CNN-based approach outperformed the segmentation traditional methods with respect to overall recognition accuracy for the dataset under investigation.","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":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122824779","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

Multi-Perspective Finger-Vein Biometrics 多视角指静脉生物识别技术

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-07-02 DOI: 10.1109/BTAS.2018.8698558

B. Prommegger, Christof Kauba, A. Uhl

引用次数: 23

Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation 全卷积网络和生成对抗网络在巩膜分割中的应用

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-06-22 DOI: 10.1109/BTAS.2018.8698597

D. Lucio, Rayson Laroca, E. Severo, A. Britto, D. Menotti

{"title":"Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation","authors":"D. Lucio, Rayson Laroca, E. Severo, A. Britto, D. Menotti","doi":"10.1109/BTAS.2018.8698597","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698597","url":null,"abstract":"Due to the world’s demand for security systems, biometrics can be seen as an important topic of research in computer vision. One of the biometric forms that has been gaining attention is the recognition based on sclera. The initial and paramount step for performing this type of recognition is the segmentation of the region of interest, i.e. the sclera. In this context, two approaches for such task based on the Fully Convolutional Network (FCN) and on Generative Adversarial Network (GAN) are introduced in this work. FCN is similar to a common convolution neural network, however the fully connected layers (i.e., the classification layers) are removed from the end of the network and the output is generated by combining the output of pooling layers from different convolutional ones. The GAN is based on the game theory, where we have two networks competing with each other to generate the best segmentation. In order to perform fair comparison with baselines and quantitative and objective evaluations of the proposed approaches, we provide to the scientific community new 1,300 manually segmented images from two databases1. The experiments are performed on the UBIRIS.v2 and MICHE databases and the best performing configurations of our propositions achieved F-score's measures of 87.48% and 88.32%, respectively.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126751491","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: 28

DocFace: Matching ID Document Photos to Selfies* DocFace:将身份证件照片与自拍相匹配*

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-05-06 DOI: 10.1109/BTAS.2018.8698596

Yichun Shi, Anil K. Jain

引用次数: 27