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

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

DNA2FACE: An approach to correlating 3D facial structure and DNA DNA2FACE:一种将三维面部结构与DNA相关联的方法

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272746

Nisha Srinivas, Ryan Tokola, A. Mikkilineni, I. Nookaew, M. Leuze, Chris Boehnen

{"title":"DNA2FACE: An approach to correlating 3D facial structure and DNA","authors":"Nisha Srinivas, Ryan Tokola, A. Mikkilineni, I. Nookaew, M. Leuze, Chris Boehnen","doi":"10.1109/BTAS.2017.8272746","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272746","url":null,"abstract":"In this paper we introduce the concept of correlating genetic variations in an individual's specific genetic code (DNA) and facial morphology. This is the first step in the research effort to estimate facial appearance from DNA samples, which is gaining momentum within intelligence, law enforcement and national security communities. The dataset for the study consisting of genetic data and 3D facial scans (phenotype) data was obtained through the FaceBase Consortium. The proposed approach has three main steps: phenotype feature extraction from 3D face images, genotype feature extraction from a DNA sample, and genome-wide association analysis to determine genetic variations that contribute to facial structure and appearance. Results indicate that there exist significant correlations between genetic information and facial structure. We have identified 30 single nucleotide polymorphisms (SNPs), i.e. genetic variations, that significantly contribute to facial structure and appearance. We conclude with a preliminary attempt at facial reconstruction from the genetic data and emphasize on the complexity of the problem and the challenges encountered.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"75 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":"122832353","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-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

Accuracy evaluation of handwritten signature verification: Rethinking the random-skilled forgeries dichotomy 手写签名验证的准确性评估:重新思考随机-熟练伪造二分法

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272711

Javier Galbally, M. Gomez-Barrero, A. Ross

引用次数: 16

Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing 基于自适应通道判别的深度卷积动态纹理学习3D蒙版人脸防欺骗

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272765

Rui Shao, X. Lan, P. Yuen

引用次数: 67

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

Conditional random fields incorporate convolutional neural networks for human eye sclera semantic segmentation

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272768

Russel Mesbah, B. McCane, S. Mills

引用次数: 8

Deep learning with time-frequency representation for pulse estimation from facial videos 基于时频表示的深度学习人脸视频脉冲估计

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272721

G. Hsu, Arulmurugan Ambikapathi, Ming Chen

{"title":"Deep learning with time-frequency representation for pulse estimation from facial videos","authors":"G. Hsu, Arulmurugan Ambikapathi, Ming Chen","doi":"10.1109/BTAS.2017.8272721","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272721","url":null,"abstract":"Accurate pulse estimation is of pivotal importance in acquiring the critical physical conditions of human subjects under test, and facial video based pulse estimation approaches recently gained attention owing to their simplicity. In this work, we have endeavored to develop a novel deep learning approach as the core part for pulse (heart rate) estimation by using a common RGB camera. Our approach consists of four steps. We first begin by detecting the face and its landmarks, and thereby locate the required facial ROI. In Step 2, we extract the sample mean sequences of the R, G, and B channels from the facial ROI, and explore three processing schemes for noise removal and signal enhancement. In Step 3, the Short-Time Fourier Transform (STFT) is employed to build the 2D Time-Frequency Representations (TFRs) of the sequences. The 2D TFR enables the formulation of the pulse estimation as an image-based classification problem, which can be solved in Step 4 by a deep Con-volutional Neural Network (CNN). Our approach is one of the pioneering works for attempting real-time pulse estimation using a deep learning framework. We have developed a pulse database, called the Pulse from Face (PFF), and used it to train the CNN. The PFF database will be made publicly available to advance related research. When compared to state-of-the-art pulse estimation approaches on the standard MAHNOB-HCI database, the proposed approach has exhibited superior performance.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"604 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":"129215996","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: 75

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