2019 International Conference on Biometrics (ICB)最新文献_第3页

Gait-Based Age Estimation with Deep Convolutional Neural Network 基于步态的深度卷积神经网络年龄估计

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987240

Shaoxiong Zhang, Yunhong Wang, Annan Li

引用次数: 14

End-to-End Protocols and Performance Metrics For Unconstrained Face Recognition 无约束人脸识别的端到端协议和性能指标

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987345

James A. Duncan, N. Kalka, Brianna Maze, Anil K. Jain

引用次数: 2

Multi-sample Compression of Finger Vein Images using H.265 Video Coding 基于H.265视频编码的手指静脉图像多样本压缩

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987412

Kevin Schörgnhofer, Sami Dafir, A. Uhl

引用次数: 0

SeLENet: A Semi-Supervised Low Light Face Enhancement Method for Mobile Face Unlock SeLENet:一种用于移动人脸解锁的半监督低光人脸增强方法

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987344

Ha A. Le, I. Kakadiaris

{"title":"SeLENet: A Semi-Supervised Low Light Face Enhancement Method for Mobile Face Unlock","authors":"Ha A. Le, I. Kakadiaris","doi":"10.1109/ICB45273.2019.8987344","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987344","url":null,"abstract":"Facial recognition is becoming a standard feature on new smartphones. However, the face unlocking feature of devices using regular 2D camera sensors exhibits poor performance in low light environments. In this paper, we propose a semi-supervised low light face enhancement method to improve face verification performance on low light face images. The proposed method is a network with two components: decomposition and reconstruction. The decomposition component splits an input low light face image into face normals and face albedo, while the reconstruction component enhances and reconstructs the lighting condition of the input image using the spherical harmonic lighting coefficients of a direct ambient white light. The network is trained in a semi-supervised manner using both labeled synthetic data and unlabeled real data. Qualitative results demonstrate that the proposed method produces more realistic images than the state-of-the-art low light enhancement algorithms. Quantitative experiments confirm the effectiveness of our low light face enhancement method for face verification. By applying the proposed method, the gap of verification accuracy between extreme low light and neutral light face images is reduced from approximately 3% to 0.5%.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116731589","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

BioPass-UFPB: a Novel Multibiometric Database biopass - fpb:一种新型的多生物特征数据库

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987313

A. Silva, H. Gomes, H. N. Oliveira, P. B. Lins, Diego F. S. Lima, L. Batista

引用次数: 0

Universal Material Translator: Towards Spoof Fingerprint Generalization 通用材料翻译器:迈向欺骗指纹泛化

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987320

Rohit Gajawada, Additya Popli, T. Chugh, A. Namboodiri, Anil K. Jain

引用次数: 31

Adversarial Examples to Fool Iris Recognition Systems 欺骗虹膜识别系统的对抗性示例

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987389

Sobhan Soleymani, Ali Dabouei, J. Dawson, N. Nasrabadi

引用次数: 14

Merged Multi-CNN with Parameter Reduction for Face Attribute Estimation 融合参数约简的Multi-CNN人脸属性估计

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987397

Hiroya Kawai, Koichi Ito, T. Aoki

引用次数: 1

Conditional Perceptual Adversarial Variational Autoencoder for Age Progression and Regression on Child Face 儿童面部年龄进退的条件知觉对抗变分自编码器

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987410

Praveen Kumar Chandaliya, N. Nain

{"title":"Conditional Perceptual Adversarial Variational Autoencoder for Age Progression and Regression on Child Face","authors":"Praveen Kumar Chandaliya, N. Nain","doi":"10.1109/ICB45273.2019.8987410","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987410","url":null,"abstract":"Recent works have shown that Generative Adversarial Networks (GAN) and Variational Auto-Encoder (VAE) can construct synthetic images of remarkable visual fidelity. In this paper, we propose a novel architecture based on GAN and VAE with Perceptual loss termed as Conditional Perceptual Adversarial Variational Autoencoder (CPAVAE), a model for face aging and rejuvenation on children face. CPAVAE performs face aging and rejuvenation by learning manifold constrained with conditions such as age and gender, which allows it to preserve face identity. CPAVAE uses six networks; these networks are an Encoder (E) and Sampling (S) which maps the child face to latent vector, Generator (G) takes the latent vector z as input along with age conditioned vector and tries to reconstruct the input image, a perceptual loss network Φ, a pre-trained very deep convolution network, discriminator on the encoder (Dz) smoothen’s the age transformation, discriminator on the image (Dimg) forces the generator to produce human realistic images. Here D and E are based on Variational Auto-encoder (VAE) architecture, VGGNet is used as perceptual loss network (Ploss), Dz and Dimg are convolutional neural networks. We represent child face progression and regression on the Children Longitudinal Face(CLF) dataset containing 10752 faces images in the age group [0 : 20]. This dataset contains 6164 and 4588 images of boys and girls respectively.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128459554","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: 11

Hyperspectral Band Selection for Face Recognition Based on a Structurally Sparsified Deep Convolutional Neural Networks 基于结构稀疏化深度卷积神经网络的人脸识别高光谱波段选择

2019 International Conference on Biometrics (ICB) Pub Date : 2019-06-01 DOI: 10.1109/ICB45273.2019.8987360

Fariborz Taherkhani, J. Dawson, N. Nasrabadi

{"title":"Hyperspectral Band Selection for Face Recognition Based on a Structurally Sparsified Deep Convolutional Neural Networks","authors":"Fariborz Taherkhani, J. Dawson, N. Nasrabadi","doi":"10.1109/ICB45273.2019.8987360","DOIUrl":"https://doi.org/10.1109/ICB45273.2019.8987360","url":null,"abstract":"Hyperspectral imaging systems collect and process information from specific wavelengths across the electromagnetic spectrum. The fusion of multi-spectral bands in the visible spectrum has been exploited to improve face recognition performance over conventional broad band face images. In this paper, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands. Specifically, in this method, all the bands are fed to a CNN and the convolutional filters in the first layer of the CNN are then regularized by employing a group Lasso algorithm to zero out the redundant bands during the training of the network. Contrary to other methods which usually select the bands manually or in a greedy fashion, our method selects the optimal spectral bands automatically to achieve the best face recognition performance over all the spectral bands. Moreover, experimental results demonstrate that our method outperforms state of the art band selection methods for face recognition on several publicly-available hyperspectral face image datasets.","PeriodicalId":430846,"journal":{"name":"2019 International Conference on Biometrics (ICB)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134457588","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