This paper aims at a newly raising task in visual surveillance: re-identifying people at a distance by matching body information, given several reference examples. Most of existing works solve this task by matching a reference template with the target individual, but often suffer from large human appearance variability (e.g. different poses/views, illumination) and high false positives in matching caused by conjunctions, occlusions or surrounding clutters. Addressing these problems, we construct a simple yet expressive template from a few reference images of a certain individual, which represents the body as an articulated assembly of compositional and alternative parts, and propose an effective matching algorithm with cluster sampling. This algorithm is designed within a candidacy graph whose vertices are matching candidates (i.e. a pair of source and target body parts), and iterates in two steps for convergence. (i) It generates possible partial matches based on compatible and competitive relations among body parts. (ii) It confirms the partial matches to generate a new matching solution, which is accepted by the Markov Chain Monte Carlo (MCMC) mechanism. In the experiments, we demonstrate the superior performance of our approach on three public databases compared to existing methods.
Pooling Faces: Template based Face Recognition with Pooled Face Images
Information Sciences Institute, USC（南加州大学信息科学研究所）；The Open University of Israel（以色列开放大学）；Institute for Robotics and Intelligent Systems, USC（南加州大学机器人与智能系统研究所）
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification
Shenzhen Institutes of Advanced Technology, Chinese Academy of Science（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Microelectronics of the Chinese Academy of Sciences（中国科学院微电子研究所）
近年来，在个人身份重新识别（re-id）方面取得了很大进展。Market 1501、CUHK 03和DukeMTMC等学术基准对促进re-id研究起到了重要作用。据我们所知，所有现有的基准都假设同一个人会有同样的衣服。而在现实世界中，人们经常会换衣服。为了解决换衣服的人的真实身份问题，我们构建了一个新的大规模的真实身份基准命名为Clothes Changing Person Set（COCAS），它提供了多个图像的同一身份与不同的衣服。COCAS共包含5，266人的62，382张身体图像。在COCAS的基础上，我们引入了一个新的人re-id设置的衣服更换问题，其中的查询既包括一个衣服模板和一个人的形象采取另一件衣服。此外，我们提出了一个名为生物特征服装网络（BC-Net）的两个分支网络，它可以有效地集成生物特征和服装特征，以实现在我们的设置下的身份识别。实验表明，利用服装模板进行服装换装识别是可行的。
Untargeted Near-collision Attacks on Biometrics: Real-world Bounds and Theoretical Limits
Université Clermont-Auvergne（克莱蒙奥弗涅大学）；CNRS（法国国家科学研究中心）；Mines de Saint-Étienne（圣埃蒂安矿业学院）；Clermont-Auvergne-INP（克莱蒙奥弗涅综合理工学院）；LIMOS（信息、建模与优化实验室）；LMBP（布莱斯·帕斯卡实验室）
Privacy-preserving Preselection for Face Identification Based on Packing
College of Electronics and Information Engineering（电子信息工程学院）；the Provincial Key Laboratory of Intelligent Communication and Digital Society Governance（智能通信与数字社会治理省级重点实验室）；Shenzhen University（深圳大学）；Sanya Institute of Hunan University of Science and Technology（湖南科技大学三亚学院）；School of Computer Science and Cyber Engineering（计算机科学与网络工程学院）；Guangzhou University（广州大学）；School of Computer Science and Technology（计算机科学与技术学院）；Changsha University of Science and Technology（长沙理工大学）；School of Civil and Environmental Engineering（土木与环境工程学院）
Identification and Classification of Phenomena in Multispectral Satellite Imagery Using a New Image Smoother Method and its Applications in Environmental Remote Sensing
Non-planar Object Detection and Identification by Features Matching and Triangulation Growth
POLITECNICO DI MILANO School of Industrial and Information Engineering（米兰理工大学工业与信息工程学院）；Master of Science Degree in Computer Science and Engineering Department of Electronics, Information and Bioengineering（电子、信息与生物工程学院）
Unsupervised SE(3) Disentanglement for in situ Macromolecular Morphology Identification from Cryo-Electron Tomography
Carnegie Mellon University（卡内基梅隆大学）；Indian Institute of Technology (IIT)（印度理工学院）；Division of CryoEM and Bioimaging SSRL SLAC National Accelerator Laboratory Stanford University（冷冻电镜与生物成像部SLAC国家加速器实验室斯坦福大学）
