2017 IEEE International Joint Conference on Biometrics (IJCB)最新文献_第3页

Multi-view 3D face reconstruction with deep recurrent neural networks 基于深度递归神经网络的多视图三维人脸重建

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272733

Pengfei Dou, I. Kakadiaris

{"title":"Multi-view 3D face reconstruction with deep recurrent neural networks","authors":"Pengfei Dou, I. Kakadiaris","doi":"10.1109/BTAS.2017.8272733","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272733","url":null,"abstract":"Image-based 3D face reconstruction has great potential in different areas, such as facial recognition, facial analysis, and facial animation. Due to the variations in image quality, single-image-based 3D face reconstruction might not be sufficient to accurately reconstruct a 3D face. To overcome this limitation, multi-view 3D face reconstruction uses multiple images of the same subject and aggregates complementary information for better accuracy. Though theoretically appealing, there are multiple challenges in practice. Among these challenges, the most significant is that it is difficult to establish coherent and accurate correspondence among a set of images, especially when these images are captured in different conditions. In this paper, we propose a method, Deep Recurrent 3D FAce Reconstruction (DRFAR), to solve the task ofmulti-view 3D face reconstruction using a subspace representation of the 3D facial shape and a deep recurrent neural network that consists of both a deep con-volutional neural network (DCNN) and a recurrent neural network (RNN). The DCNN disentangles the facial identity and the facial expression components for each single image independently, while the RNN fuses identity-related features from the DCNN and aggregates the identity specific contextual information, or the identity signal, from the whole set of images to predict the facial identity parameter, which is robust to variations in image quality and is consistent over the whole set of images. Through extensive experiments, we evaluate our proposed method and demonstrate its superiority over existing methods.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126606987","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: 39

On the guessability of binary biometric templates: A practical guessing entropy based approach 二元生物特征模板的可猜测性:一种实用的基于猜测熵的方法

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272719

Guangcan Mai, M. Lim, P. Yuen

引用次数: 3

Location-sensitive sparse representation of deep normal patterns for expression-robust 3D face recognition 表达鲁棒3D人脸识别中深度正常模式的位置敏感稀疏表示

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272703

Huibin Li, Jian Sun, Liming Chen

{"title":"Location-sensitive sparse representation of deep normal patterns for expression-robust 3D face recognition","authors":"Huibin Li, Jian Sun, Liming Chen","doi":"10.1109/BTAS.2017.8272703","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272703","url":null,"abstract":"This paper presents a straight-forward yet efficient, and expression-robust 3D face recognition approach by exploring location sensitive sparse representation of deep normal patterns (DNP). In particular, given raw 3D facial surfaces, we first run 3D face pre-processing pipeline, including nose tip detection, face region cropping, and pose normalization. The 3D coordinates of each normalized 3D facial surface are then projected into 2D plane to generate geometry images, from which three images of facial surface normal components are estimated. Each normal image is then fed into a pre-trained deep face net to generate deep representations of facial surface normals, i.e., deep normal patterns. Considering the importance of different facial locations, we propose a location sensitive sparse representation classifier (LS-SRC) for similarity measure among deep normal patterns associated with different 3D faces. Finally, simple score-level fusion of different normal components are used for the final decision. The proposed approach achieves significantly high performance, and reporting rank-one scores of 98.01%, 97.60%, and 96.13% on the FRGC v2.0, Bosphorus, and BU-3DFE databases when only one sample per subject is used in the gallery. These experimental results reveals that the performance of 3D face recognition would be constantly improved with the aid of training deep models from massive 2D face images, which opens the door for future directions of 3D face recognition.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128974683","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

ICFVR 2017: 3rd international competition on finger vein recognition ICFVR 2017:第三届手指静脉识别国际比赛

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272760

Yi Zhang, Houjun Huang, Haifeng Zhang, Liao Ni, W. Xu, N. U. Ahmed, Md. Shakil Ahmed, Yilun Jin, Ying Chen, Jingxuan Wen, Wenxin Li

引用次数: 3

In defense of low-level structural features and SVMs for facial attribute classification: Application to detection of eye state, Mouth State, and eyeglasses in the wild 基于低级结构特征和支持向量机的面部属性分类:在野外眼睛状态、嘴巴状态和眼镜状态检测中的应用

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272747

Abdulaziz Alorf, A. L. Abbott

{"title":"In defense of low-level structural features and SVMs for facial attribute classification: Application to detection of eye state, Mouth State, and eyeglasses in the wild","authors":"Abdulaziz Alorf, A. L. Abbott","doi":"10.1109/BTAS.2017.8272747","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272747","url":null,"abstract":"The current trend in image analysis is to employ automatically detected feature types, such as those obtained using deep-learning techniques. For some applications, however, manually crafted features such as Histogram of Oriented Gradients (HOG) continue to yield better performance in demanding situations. This paper considers both approaches for the problem of facial attribute classification, for images obtained “in the wild.” Attributes of particular interest are eye state (open/closed), mouth state (open/closed), and eyeglasses (present/absent). We present a full face-processing pipeline that employs conventional machine learning techniques, from detection to attribute classification. Experimental results have indicated better performance using RootSIFT with a conventional support-vector machine (SVM) approach, as compared to deep-learning approaches that have been reported in the literature. Our proposed open/closed eye classifier has yielded an accuracy of 99.3% on the CEW dataset, and an accuracy of 98.7% on the ZJU dataset. Similarly, our proposed open/closed mouth classifier has achieved performance similar to deep learning. Also, our proposed presence/absence eyeglasses classifier delivered very good performance, being the best method on LFWA, and second best for the CelebA dataset. The system reported here runs at 30 fps on HD-sized video using a CPU-only implementation.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609026","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: 6

Towards pre-alignment of near-infrared iris images 近红外虹膜图像的预对准

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272718

P. Drozdowski, C. Rathgeb, H. Hofbauer, J. Wagner, A. Uhl, C. Busch

{"title":"Towards pre-alignment of near-infrared iris images","authors":"P. Drozdowski, C. Rathgeb, H. Hofbauer, J. Wagner, A. Uhl, C. Busch","doi":"10.1109/BTAS.2017.8272718","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272718","url":null,"abstract":"The necessity of biometric template alignment imposes a significant computational load and increases the probability of false positive occurrences in biometric systems. While for some modalities, automatic pre-alignment of biometric samples is utilised, this topic has not yet been explored for systems based on the iris. This paper presents a method for pre-alignment of iris images based on the positions ofautomatically detected eye corners. Existing work in the area of automatic eye corner detection has hitherto only involved visible wavelength images; for the near-infrared images, used in the vast majority of current iris recognition systems, this task is significantly more challenging and as of yet unexplored. A comparative study of two methods for solving this problem is presented in this paper. The eye corners detected by the two methods are then used for the pre-alignment and biometric performance evaluation experiments. The system utilising image pre-alignment is benchmarked against a baseline iris recognition system on the iris subset of the BioSecure database. In the benchmark, the workload associated with alignment compensation is significantly reduced, while the biometric performance remains unchanged or even improves slightly.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114911039","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

Computing an image Phylogeny Tree from photometrically modified iris images 从光度法修改的虹膜图像计算图像系统发育树

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272749

Sudipta Banerjee, A. Ross

引用次数: 2

Extracting sub-glottal and Supra-glottal features from MFCC using convolutional neural networks for speaker identification in degraded audio signals 利用卷积神经网络提取声门下和上声门特征，用于退化音频信号的说话人识别

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272748

Anurag Chowdhury, A. Ross

引用次数: 10

Face anti-spoofing using patch and depth-based CNNs 使用补丁和基于深度的cnn进行人脸防欺骗

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272713

Yousef Atoum, Yaojie Liu, Amin Jourabloo, Xiaoming Liu

引用次数: 325

Learning optimised representations for view-invariant gait recognition 视觉不变步态识别的学习优化表征

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272769

Ning Jia, Victor Sanchez, Chang-Tsun Li

引用次数: 7