2016 International Conference on Biometrics (ICB)最新文献_第5页

Multi-scale dictionaries based fingerprint orientation field estimation 基于多尺度字典的指纹方向场估计

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550071

Chunjie Chen, Jianjiang Feng, Jie Zhou

引用次数: 10

Accurate iris segmentation in non-cooperative environments using fully convolutional networks 基于全卷积网络的非合作环境下虹膜准确分割

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550055

Nianfeng Liu, Haiqing Li, Man Zhang, Jing Liu, Zhenan Sun, T. Tan

{"title":"Accurate iris segmentation in non-cooperative environments using fully convolutional networks","authors":"Nianfeng Liu, Haiqing Li, Man Zhang, Jing Liu, Zhenan Sun, T. Tan","doi":"10.1109/ICB.2016.7550055","DOIUrl":"https://doi.org/10.1109/ICB.2016.7550055","url":null,"abstract":"Conventional iris recognition requires controlled conditions (e.g., close acquisition distance and stop-and-stare scheme) and high user cooperation for image acquisition. Non-cooperative acquisition environments introduce many adverse factors such as blur, off-axis, occlusions and specular reflections, which challenge existing iris segmentation approaches. In this paper, we present two iris segmentation models, namely hierarchical convolutional neural networks (HCNNs) and multi-scale fully convolutional network (MFCNs), for noisy iris images acquired at-a-distance and on-the-move. Both models automatically locate iris pixels without handcrafted features or rules. Moreover, the features and classifiers are jointly optimized. They are end-to-end models which require no further pre- and post-processing and outperform other state-of-the-art methods. Compared with HCNNs, MFCNs take input of arbitrary size and produces correspondingly-sized output without sliding window prediction, which makes MFCNs more efficient. The shallow, fine layers and deep, global layers are combined in MFCNs to capture both the texture details and global structure of iris patterns. Experimental results show that MFCNs are more robust than HCNNs to noises, and can greatly improve the current state-of-the-arts by 25.62% and 13.24% on the UBIRIS.v2 and CASIA.v4-distance databases, respectively.","PeriodicalId":308715,"journal":{"name":"2016 International Conference on Biometrics (ICB)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128678016","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: 158

Two-step calibration method for multi-algorithm score-based face recognition systems by minimizing discrimination loss 基于分数的多算法人脸识别系统的两步标定方法

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550094

N. Susyanto, R. Veldhuis, L. Spreeuwers, C. Klaassen

引用次数: 3

Scale space texture analysis for face anti-spoofing 人脸抗欺骗的尺度空间纹理分析

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550078

Z. Boulkenafet, Jukka Komulainen, Xiaoyi Feng, A. Hadid

引用次数: 32

User-specific effects in Fingerprint Presentation Attacks Detection: Insights for future research 指纹表示攻击检测中的用户特定效果:对未来研究的见解

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550081

Luca Ghiani, G. Marcialis, F. Roli, Pierluigi Tuveri

引用次数: 4

ICB-RW 2016: International challenge on biometric recognition in the wild ICB-RW 2016:野外生物识别的国际挑战

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550066

J. Neves, Hugo Proença

引用次数: 13

Biometric recognition of surgically altered periocular region: A comprehensive study 手术改变眼周区域的生物识别:一项综合研究

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-13 DOI: 10.1109/ICB.2016.7550070

K. Raja, Ramachandra Raghavendra, C. Busch

{"title":"Biometric recognition of surgically altered periocular region: A comprehensive study","authors":"K. Raja, Ramachandra Raghavendra, C. Busch","doi":"10.1109/ICB.2016.7550070","DOIUrl":"https://doi.org/10.1109/ICB.2016.7550070","url":null,"abstract":"Wide acceptance of biometrics as an authentication mode has led to investigation of multiple modalities such as face, periocular, iris for the long term robustness. Due to various deformities arising out of deteriorating health, need for enhancing the beauty by choice or to fix the injury as a result of trauma or aging, people tend to undergo surgery. However, such surgeries do not guarantee the restoration of physical biometric characteristics (face, periocular, iris etc.) to original appearance and thereby impacting the performance in biometric identifications. Among many physical biometric characteristics, periocular recognition is widely accepted for authentication purposes. This work studies the impact of periocular surgeries on biometric performance. To this extent, we introduce a new large scale periocular surgery database comprising of 402 unique periocular images acquired before and after the surgery. This is the first work that provides comprehensive study on evaluating the impact of surgeries in periocular region on periocular recognition. Extensive experiments are carried out on a newly created dataset using 11 different state-of-art periocular recognition schemes. Further, we also explore score level fusion of these algorithms. Results obtained on the newly created large scale database indicate the degraded identification performance of both the state-of-art and fusion algorithms.","PeriodicalId":308715,"journal":{"name":"2016 International Conference on Biometrics (ICB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123761176","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

Bootstrapping Joint Bayesian model for robust face verification 鲁棒人脸验证的自举联合贝叶斯模型

2016 International Conference on Biometrics (ICB) Pub Date : 2016-06-01 DOI: 10.1109/ICB.2016.7550088

Cheng Cheng, Junliang Xing, Youji Feng, Deling Li, Xiangdong Zhou

引用次数: 4

Face attribute prediction using off-the-shelf CNN features 使用现成的CNN特征进行人脸属性预测

2016 International Conference on Biometrics (ICB) Pub Date : 2016-02-12 DOI: 10.1109/ICB.2016.7550092

Yang Zhong, Josephine Sullivan, Haibo Li

{"title":"Face attribute prediction using off-the-shelf CNN features","authors":"Yang Zhong, Josephine Sullivan, Haibo Li","doi":"10.1109/ICB.2016.7550092","DOIUrl":"https://doi.org/10.1109/ICB.2016.7550092","url":null,"abstract":"Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks - face localization, facial descriptor construction, and attribute classification - in a pipeline. As a typical classification problem, face attribute prediction has been addressed using deep learning. Current state-of-the-art performance was achieved by using two cascaded Convolutional Neural Networks (CNNs), which were specifically trained to learn face localization and attribute description. In this paper, we experiment with an alternative way of employing the power of deep representations from CNNs. Combining with conventional face localization techniques, we use off-the-shelf architectures trained for face recognition to build facial descriptors. Recognizing that the describable face attributes are diverse, our face descriptors are constructed from different levels of the CNNs for different attributes to best facilitate face attribute prediction. Experiments on two large datasets, LFWA and CelebA, show that our approach is entirely comparable to the state-of-the-art. Our findings not only demonstrate an efficient face attribute prediction approach, but also raise an important question: how to leverage the power of off-the-shelf CNN representations for novel tasks.","PeriodicalId":308715,"journal":{"name":"2016 International Conference on Biometrics (ICB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129261239","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: 95