2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)最新文献_第3页

On Matching Faces with Alterations due to Plastic Surgery and Disguise 关于整形手术和伪装造成的面部变化的匹配

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698571

Saksham Suri, A. Sankaran, Mayank Vatsa, Richa Singh

引用次数: 20

Validation of Biometric Identification of Dairy Cows based on Udder NIR Images 基于乳房近红外图像的奶牛生物特征识别验证

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698553

Benjamin Schilling, Keivan Bahmani, Boyang Li, Sean Banerjee, Jessica Scillieri Smith, T. Moshier, S. Schuckers

引用次数: 3

Spoofing Deep Face Recognition with Custom Silicone Masks 欺骗深度人脸识别定制硅胶面具

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698550

Sushil K. Bhattacharjee, A. Mohammadi, S. Marcel

引用次数: 75

Geometrically Consistent Pedestrian Trajectory Extraction for Gait Recognition 用于步态识别的几何一致行人轨迹提取

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698559

Yasushi Makihara, Gakuto Ogi, Y. Yagi

{"title":"Geometrically Consistent Pedestrian Trajectory Extraction for Gait Recognition","authors":"Yasushi Makihara, Gakuto Ogi, Y. Yagi","doi":"10.1109/BTAS.2018.8698559","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698559","url":null,"abstract":"In the gait recognition community, silhouette-based gait representations such as gait energy image have been widely employed for the last decade. In order to obtain good quality of gait features, it is essential to get a well aligned silhouette sequence, which is, however, not necessarily easy with imperfect silhouettes and also with scale and position changes due to perspective projection. We therefore propose a gait recognition-oriented approach to pedestrian trajectory extraction, i. e., bounding box sequence for each pedestrian. More specifically, we firstly developed an inter-active tool to get camera calibration parameters for a target scene geometry without on-site workload. We then introduce a geometric constraint to better keep the consistency of bounding boxes among frames w.r.t. the pedestrian ’s height and foot bottom points on the ground plane. Sub-sequently, we apply analytical dynamic programing (DP) repeatedly to find multiple pedestrian ’s trajectories on the ground plane, where data and transition scores are computed based on semantic segmentation results and color histogram similarity. Moreover, since DP just considers the smoothness between adjacent frames, we approximate the trajectory by a piece-wise linear trajectory to make it more globally smooth. Experimental results show that the proposed method enables us to make better aligned gait features and consequently improves gait recognition accuracy.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116475999","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

Deep Learning Frameworks for Off-Angle Iris Recognition 非角度虹膜识别的深度学习框架

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698565

M. Karakaya

{"title":"Deep Learning Frameworks for Off-Angle Iris Recognition","authors":"M. Karakaya","doi":"10.1109/BTAS.2018.8698565","DOIUrl":"https://doi.org/10.1109/BTAS.2018.8698565","url":null,"abstract":"Although iris is known as one of the most accurate, distinctive, and reliable biometric identification, the accuracy of iris recognition depends on the image quality and it is negatively affected by several factors such as gaze angle, occlusion, and dilation. Since standoff iris recognition systems are much less constrained than traditional systems, the iris images captured are likely to be non-ideal, off-angle, and dilated. In this paper, we present a deep learning algorithm to improve performance of off-angle iris recognition in traditional and untraditional iris recognition frameworks. As a main contribution, this approach will allow us to study the effect of the gaze angle in ocular/periocular biometrics and fuse the information in different off-angle iris images. Using convolutional neural networks (CNNs), we first investigate traditional iris recognition framework with segmentation, normalization, and CNN based encoding and matching. In nontraditional framework, we use the iris images without segmentation and normalization to investigate the effect of periocular regions. We train and test our deep learning based approach with frontal and off-angle iris images in different sizes and types such as original, cropped, segmented, and masked images. Based on results from our real off-angle iris image dataset with 52 subjects, the proposed method improved the recognition performance compared with traditional off-angle iris recognition approaches.","PeriodicalId":409205,"journal":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","volume":"79 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120898270","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

PRNU Variance Analysis for Morphed Face Image Detection 变形人脸图像检测的PRNU方差分析

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698576

L. Debiasi, C. Rathgeb, U. Scherhag, A. Uhl, C. Busch

引用次数: 33

Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition 基于异质性感知的移动眼周识别深度嵌入

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698551

Rishabh Garg, Yashasvi Baweja, Soumyadeep Ghosh, Mayank Vatsa, Richa Singh, N. Ratha

引用次数: 12

Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis 基于属性保留合成的极化热对可见人脸的验证

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698554

Xing Di, He Zhang, Vishal M. Patel

引用次数: 32

LivDet 2017 - Fingerprint Systems Liveness Detection Competition LivDet 2017 -指纹系统活性检测大赛

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698578

David Yambay, S. Schuckers, Samantha Denning, C. Sandmann, Andrey Bachurinski, J. Hogan

引用次数: 8

Pulse-based Features for Face Presentation Attack Detection 基于脉冲特征的人脸呈现攻击检测

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) Pub Date : 2018-10-01 DOI: 10.1109/BTAS.2018.8698579

G. Heusch, S. Marcel

引用次数: 20