2017 IEEE International Joint Conference on Biometrics (IJCB)最新文献_第2页

Robust face presentation attack detection on smartphones : An approach based on variable focus 基于可变焦点的智能手机鲁棒人脸呈现攻击检测方法

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272753

K. Raja, P. Wasnik, Ramachandra Raghavendra, C. Busch

{"title":"Robust face presentation attack detection on smartphones : An approach based on variable focus","authors":"K. Raja, P. Wasnik, Ramachandra Raghavendra, C. Busch","doi":"10.1109/BTAS.2017.8272753","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272753","url":null,"abstract":"Smartphone based facial biometric systems have been well used in many of the security applications starting from simple phone unlocking to secure banking applications. This work presents a new approach of exploring the intrinsic characteristics of the smartphone camera to capture a number of stack images in the depth-of-field. With the set of stack images obtained, we present a new feature-free and classifier-free approach to provide the presentation attack resistant face biometric system. With the entire system implemented on the smartphone, we demonstrate the applicability of the proposed scheme in obtaining a stack of images with varying focus to effectively determine the presentation attacks. We create a new database of 13250 images at different focal length to present a detailed analysis of vulnerability together with the evaluation of proposed scheme. An extensive evaluation of the newly created database comprising of 5 different Presentation Attack Instruments (PAI) has demonstrated an outstanding performance on all 5 PAI through proposed approach. With the set ofcomplementary benefits of proposed approach illustrated in this work, we deduce the robustness towards unseen 2D attacks.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"33 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":"121087894","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: 2

Using associative classification to authenticate mobile device users 使用关联分类验证移动设备用户

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272684

T. Neal, D. Woodard

引用次数: 7

Shared dataset on natural human-computer interaction to support continuous authentication research 自然人机交互共享数据集，支持持续认证研究

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272738

Chris Murphy, Jiaju Huang, Daqing Hou, S. Schuckers

引用次数: 29

Towards open-set face recognition using hashing functions 利用哈希函数实现开集人脸识别

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272751

R. H. Vareto, Samira Silva, F. Costa, W. R. Schwartz

引用次数: 29

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

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

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

Finger vein image retrieval via affinity-preserving K-means hashing 基于亲和保持k均值哈希的手指静脉图像检索

2017 IEEE International Joint Conference on Biometrics (IJCB) Pub Date : 2017-10-01 DOI: 10.1109/BTAS.2017.8272720

Kun Su, Gongping Yang, Lu Yang, Yilong Yin

引用次数: 5

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

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