2011 International Joint Conference on Biometrics (IJCB)最新文献_第7页

Two faces are better than one: Face recognition in group photographs 两张脸比一张脸好:集体照片中的人脸识别

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117516

O. K. Manyam, Neeraj Kumar, P. Belhumeur, D. Kriegman

{"title":"Two faces are better than one: Face recognition in group photographs","authors":"O. K. Manyam, Neeraj Kumar, P. Belhumeur, D. Kriegman","doi":"10.1109/IJCB.2011.6117516","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117516","url":null,"abstract":"Face recognition systems classically recognize people individually. When presented with a group photograph containing multiple people, such systems implicitly assume statistical independence between each detected face. We question this basic assumption and consider instead that there is a dependence between face regions from the same image; after all, the image was acquired with a single camera, under consistent lighting (distribution, direction, spectrum), camera motion, and scene/camera geometry. Such naturally occurring commonalities between face images can be exploited when recognition decisions are made jointly across the faces, rather than independently. Furthermore, when recognizing people in isolation, some features such as color are usually uninformative in unconstrained settings. But by considering pairs of people, the relative color difference provides valuable information. This paper reconsiders the independence assumption, introduces new features and methods for recognizing pairs of individuals in group photographs, and demonstrates a marked improvement when these features are used in joint decision making vs. independent decision making. While these features alone are only moderately discriminative, we combine these new features with state-of-art attribute features and demonstrate effective recognition performance. Initial experiments on two datasets show promising improvements in accuracy.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125100639","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: 31

Face recognition in low-resolution videos using learning-based likelihood measurement model 基于学习的似然测量模型在低分辨率视频中的人脸识别

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117514

S. Biswas, G. Aggarwal, P. Flynn

引用次数: 19

Evaluation of gender classification methods on thermal and near-infrared face images 热、近红外人脸图像性别分类方法评价

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117544

Cunjian Chen, A. Ross

引用次数: 62

On matching latent to latent fingerprints 潜在指纹与潜在指纹的匹配

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117525

A. Sankaran, Tejas I. Dhamecha, Mayank Vatsa, Richa Singh

引用次数: 66

Study on the BeiHang Keystroke Dynamics Database 北航击键动力学数据库的研究

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117485

Yilin Li, Baochang Zhang, Yao Cao, Sanqiang Zhao, Yongsheng Gao, Jianzhuang Liu

引用次数: 52

Score-level fusion based on the direct estimation of the Bayes error gradient distribution 基于直接估计贝叶斯误差梯度分布的分数级融合

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117532

Yasushi Makihara, D. Muramatsu, Y. Yagi, Md. Altab Hossain

{"title":"Score-level fusion based on the direct estimation of the Bayes error gradient distribution","authors":"Yasushi Makihara, D. Muramatsu, Y. Yagi, Md. Altab Hossain","doi":"10.1109/IJCB.2011.6117532","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117532","url":null,"abstract":"This paper describes a method of score-level fusion to optimize a Receiver Operating Characteristic (ROC) curve for multimodal biometrics. When the Probability Density Functions (PDFs) of the multimodal scores for each client and imposter are obtained from the training samples, it is well known that the isolines of a function of probabilistic densities, such as the likelihood ratio, posterior, or Bayes error gradient, give the optimal ROC curve. The success of the probability density-based methods depends on the PDF estimation for each client and imposter, which still remains a challenging problem. Therefore, we introduce a framework of direct estimation of the Bayes error gradient that bypasses the troublesome PDF estimation for each client and imposter. The lattice-type control points are allocated in a multiple score space, and the Bayes error gradients on the control points are then estimated in a comprehensive manner in the energy minimization framework including not only the data fitness of the training samples but also the boundary conditions and monotonic increase constraints to suppress the over-training. The experimental results for both simulation and real public data show the effectiveness of the proposed method.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128378777","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

Speech cryptographic key regeneration based on password 基于密码的语音密码密钥再生

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117553

K. Inthavisas, D. Lopresti

引用次数: 10

Learning weighted sparse representation of encoded facial normal information for expression-robust 3D face recognition 学习编码面部常态信息的加权稀疏表示，用于表情鲁棒性三维人脸识别

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117555

Huibin Li, Di Huang, J. Morvan, Liming Chen

引用次数: 13

Mining patterns of orientations and magnitudes for face recognition 面向人脸识别的方向和大小模式挖掘

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117538

Ngoc-Son Vu, A. Caplier

{"title":"Mining patterns of orientations and magnitudes for face recognition","authors":"Ngoc-Son Vu, A. Caplier","doi":"10.1109/IJCB.2011.6117538","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117538","url":null,"abstract":"Good face recognition system is one which quickly delivers high accurate results to the end user. For this purpose, face representation must be robust, discriminative and also of low computational cost in both terms of time and space. Inspired by recently proposed feature set so-called POEM (Patterns of Oriented Edge Magnitudes) which considers the relationships between edge distributions of different image patches and is argued balancing well the three concerns, this work proposes to further exploit patterns of both orientations and magnitudes for building more efficient algorithm. We first present novel features called Patterns of Dominant Orientations (PDO) which consider the relationships between “dominant” orientations of local image regions at different scales. We also propose to apply the whitened PCA technique upon both the POEM and PDO based representations to get more compact and discriminative face descriptors. We then show that the two methods have complementary strength and that by combining the two descriptors, one obtains stronger results than either of them considered separately. By experiments carried out on several common benchmarks, including both frontal and non-frontal FERET as well as the AR datasets, we prove that our approach is more efficient than contemporary ones.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125936443","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

Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study 基于眉形特征的生物特征识别与性别分类的可行性研究

2011 International Joint Conference on Biometrics (IJCB) Pub Date : 2011-10-11 DOI: 10.1109/IJCB.2011.6117511

Yujie Dong, D. Woodard

{"title":"Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study","authors":"Yujie Dong, D. Woodard","doi":"10.1109/IJCB.2011.6117511","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117511","url":null,"abstract":"A wide variety of applications in forensic, government, and commercial fields require reliable personal identification. However, the recognition performance is severely affected when encountering non-ideal images caused by motion blur, poor contrast, various expressions, or illumination artifacts. In this paper, we investigated the use of shape-based eyebrow features under non-ideal imaging conditions for biometric recognition and gender classification. We extracted various shape-based features from the eyebrow images and compared three different classification methods: Minimum Distance Classifier (MD), Linear Discriminant Analysis Classifier (LDA) and Support Vector Machine Classifier (SVM). The methods were tested on images from two publicly available facial image databases: The Multiple Biometric Grand Challenge (MBGC) database and the Face Recognition Grand Challenge (FRGC) database. Obtained recognition rates of 90% using the MBGC database and 75% using the FRGC database as well as gender classification recognition rates of 96% and 97% for each database respectively, suggests the shape-based eyebrow features maybe be used for biometric recognition and soft biometric classification.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121577188","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: 54