Human (re)identification is a critical process for tracking, understanding a person’s activity and security authentication in a large space monitored by a camera network. Great progress has been made in this area, focusing on heterogeneous cues (face, body (2D appearance and 3D volume), other unimodal biometrics such as finger and palm, gait, behavioral cues in general) which do not require user’s collaboration. However, this problem is far from being completely solved, particularly in real-world applications under uncontrolled environments, where a large number of factors hinder the identification performance, including lighting variations, different types of occlusion, large pose and view change.
The mission of the workshop is to explore the cutting edge research in non-collaborative (re)identification, with a particular emphasis on the fusion of different modalities. For example, the face recognition and the re-identification communities, even though they share many objectives, they rarely have interacted to hybridize novel recognition applications, where both the biometric patterns (face and body) can be jointly exploited. This holds true also for the communities of gait recognition and body re-identification, thermal body recognition, visual body recognition and other biometrics cues such as Iris Recognition at a distance. The HIS workshop, in this sense, will be highly interdisciplinary, encouraging papers (even preliminary), where the modality fusion plays a primary role.
In addition, the HIS greatly relies on the development of feature and similarity learning strategy. Therefore, the HIS workshop also aims to explore recent progress in feature and similarity learning (distance metric learning) for identification. It has been observed in recent years that the (re-)identification performance can be largely improved when a robust feature representation or an appropriate distance/similarity function has been learned. In this aspect, this workshop will help the community to better understand the challenges and opportunities of feature and similarity learning techniques and their applications to (re-)identification for the next few years.
Topics of interest include, but are not limited to:
1. Face, Finger, Iris, Palm Recognition
2. Person Re-identification
3. People Detection, Tracking, and Gait analysis
4. Novel biometrics sensing methods and Soft Biometrics
5. Feature Learning for Biometrics Recognition
6. Similarity Learning (/Distance Metric Learning) for Biometrics Recognition
7. Human identification with multiple cues and multi-modality fusion
8. Large scale search and matching for identification
9. Transfer Learning for visual surveillance
10. Performance modeling, prediction and evaluation of identification/biometrics systems
11. Security improvement assessment for multi-identification/biometrics systems
This is a merge of two previous workshops in ACCV 2014: 1) Workshop on Human Identification for Surveillance (HIS), and 2) Workshop on Feature and Similarity Learning for Computer Vision (FSLCV). The merged workshop has a strong focus on the identification problem in visual surveillance with a particular concern on the feature learning and similarity learning which have benefitted many surveillance tasks.
Wei-Shi Zheng, Sun Yat-sen University, China
Ruiping Wang, Institute of Computing Technology, Chinese Academy of Sciences, China
Weihong Deng, Beijing University of Posts and Telecommunications, China
Shenghua Gao, ShanghaiTech University, China
Serge J. Belongie, University of California, San Diego, USA
Josef Bigun, Halmstad University, Sweden
Andrea Cavallaro, Queen Mary University of London, UK
Xilin Chen, Chinese Academy of Sciences, China
Amit Roy-Chowdhary, University of California, Riverside, USA
Roberto Cipolla, University of Cambridge, England
Trevor Darrell, University of California, Berkeley, USA
Hazim Ekernel, Istanbul Technical University (ITU), Turkey
Xin Geng, Southeast University, China
Shaogang Gong, Queen Mary University of London, UK
Patrick Grother, NIST, USA
Anton van den Hengel, The University of Adelaide, Australia
Timothy Hospedales, Queen Mary University of London, UK
Kaiqi Huang, Chinese Academy of Sciences, China
Di Huang, Beihang University, China
Frederic Jurie, University of Caen, France
Jaihie Kim, Yonsei University, South Korea
T-K Kim, Imperial College London, UK
Jianhuang Lai, Sun Yat-sen University, China
Simon Lucey, Carnegie Mellon University, USA
Jean-Marc Odobez, Idiap Research Institute, Switzerland
Gang Pan, Zhejiang University, China
Ian Reid, University of Oxford, UK
Fabio Roli, University of Cagliari, Italy
Peter Roth, Graz University of Technology, Austria
Pinar Duygulu Sahin, Bilkent University, Turkey
Shiguang Shan, Chinese Academy of Sciences, China
Xiaogang Wang, The Chinese University of Hong Kong, Hong Kong China
Xiaoyu Wang, University of Missouri, USA
Pramod Varshney, Syracuse University, USA
Jim Wayman, San Jose State University, USA
Jian Yang, Nanjing University of Science and Technology, China
David Zhang, Polytech University, Hong Kong
Lei Zhang, The Hong Kong Polytechnic University, Hong Kong China
Full-day workshop (e.g. 9:00am~12:30pm, 2:00pm~5:30pm)
8 oral presentations plus 18 (estimated) posters. Each oral presentation will be given 20 minutes including Q&A time.
2 Invited Talk, 40 minutes for each (if funds are available).
Poster session will be given one and half an hour in total (including break), but the posters will be set up through the whole workshop.
Paper Submission and Review
The format of any submitted paper should follow the one for ACCV2016.
This workshop will select one best paper.
Paper Submission due: Sep 8, 2016
Announcement of paper decisions: Sep 20, 2016
Workshop and Tutorial Dates: Nov 24, 2016
For the papers which have ever been submitted to ACCV but been rejected, the authors are encouraged to submit their ACCV comments by printing their ACCV comments from the CMT system as PDF file and submitting this PDF file as supplementary file along with their submission to the workshop.
||Invited Talk: Given by Wen-Huang Cheng, Chen Change Loy|
Multi-cue Information Fusion for Two-layer Activity Recognition, Yanli Ji, Jiaming Li, Hong Cheng, Xing Xu, Jingkuan Song
||Piecewise Video Condensation for Complex Scenes, Yingying Chen, La Zhang, jinqiao Wang, Hanqing Lu|
||Unsupervised Person Re-identification via Graph-Structured Image Matching, Bolei Xu, Guoping Qiu|
||Invited Talk: Given by Chen Change Loy|
||Saliency-Based Person Re-Identification by Probability Histogram, Zhang Zongyan, Cairong Zhao|
||Gait Gate: An Online Walk-through Multimodal Biometric Verification System using a Single RGB-D Sensor, Mohamed Hasan, Yasushi Makihara, Daigo Muramatsu, Yasushi Yagi|
||3D Object Recognition with Enhanced Grassmann Discriminant Analysis, Lincon Souza, Kazuhiro Fukui, Hideitsu Hino|
||An Extended Sparse Classiﬁcation Framework for Domain Adaptation in Video Surveillance, Farshad Nourbakhsh, Eric granger, Giorgio Fumera|
||BCP-BCS: Best-Fit Cascaded Matching Paradigm with Cohort Selection using Bezier Curve for Individual Recognition, Jogendra Garain, Adarsh Shah, Ravi Kumar, Dakshina Kisku, Goutam Sanyal|
· Sun Yat-sen University, China
· HomePage: http://isee.sysu.edu.cn/~zhwshi/
· Email: email@example.com
· Brief CV:
Wei-Shi Zheng received the PhD degree in applied mathematics from Sun Yat-Sen University in 2008. He is now a professor at Sun Yat-sen University. He had been a postdoctoral researcher on the EU FP7 SAMURAI Project at Queen Mary University of London and an associate professor at Sun Yat-sen University after that. He has now published more than 80 papers, including more than 40 publications in main journals (TPAMI,TNN,TIP,TSMC-B,PR) and top conferences (ICCV, CVPR,IJCAI,AAAI). He has joined the organization of four tutorial presentations in ACCV 2012, ICPR 2012, ICCV 2013 and CVPR 2015 along with other colleagues. His research interests include person/object association and activity understanding in visual surveillance. He has joined Microsoft Research Asia Young Faculty Visiting Program. He is a recipient of excellent young scientists fund of the national natural science foundation of China, and a recipient of Royal Society-Newton Advanced Fellowship.
· Institute of Computing Technology, Chinese Academy of Sciences, China
· HomePage: http://vipl.ict.ac.cn/homepage/rpwang/index.htm
· Email: firstname.lastname@example.org
· Brief CV:
Ruiping Wang is an Associate Professor at the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS). He has published more than 40 papers in peer-reviewed journals and conferences, including IEEE TPAMI, TIP, TMM, PR, CVPR, ICCV, ICML, and has received the Best Student Poster Award Runner-up from IEEE CVPR 2008 for the work on Manifold-Manifold Distance. He serves as regular reviewer/PC member for a number of leading journals and conferences, e.g. IEEE TPAMI, TIP, TCSVT, TMM, TNNLS, IJCV, ICCV, CVPR, and ECCV. He has organized tutorials in ACCV 2014 and CVPR 2015 with his colleagues. He has given invited talks in workshops of ICME 2014 and ACCV 2014. His current research interests include video-based face recognition/retrieval, facial expression analysis, image set classification, distance metric learning, and manifold learning. He is a member of the IEEE.
· Beijing University of Posts and Telecommunications, China
· HomePage: http://www.whdeng.cn/
· Email: email@example.com
· Brief CV:
Weihong Deng received the B.E. degree in information engineering and the Ph.D. degree in signal and information processing from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2004 and 2009, respectively. From Oct. 2007 to Dec. 2008, he was a postgraduate exchange student in the School of Information Technologies, University of Sydney, Australia. He is currently an associate professor in School of Information and Telecommunications Engineering, BUPT. His research interests include statistical pattern recognition and computer vision, with a particular emphasis in face recognition. He has published over 50 technical papers in international journals and conferences, such as IEEE TPAMI and CVPR. He also serves as the reviewer for several international journals, such as IEEE TPAMI / TIP / TIFS / TNNLS / TMM / TSMC, IJCV, PR / PRL. Recently, he gives tutorials on face recognition at ICME 2014, ACCV 2014, CVPR2015 and FG2015, and organizes the workshop on feature and similarity learning in ACCV2014 with colleagues. His Dissertation titled “Highly accurate face recognition algorithms” was awarded the Outstanding Doctoral Dissertation Award by Beijing Municipal Commission of Education in 2011. He has been supported by the program for New Century Excellent Talents by the Ministry of Education of China in 2013 and Beijing Nova Program in 2016.
· ShanghaiTech University, China
· HomePage: http://sist.shanghaitech.edu.cn/faculty/gaoshh/
· Email: firstname.lastname@example.org
· Brief CV:
Shenghua Gao is an assistant professor in ShanghaiTech University, China. He received the B.E. degree from the University of Science and Technology of China in 2008 (outstanding graduates), and received the Ph.D. degree from the Nanyang Technological University in 2012. From Jun 2012 to Aug 2014, he worked as a research scientist in Advanced Digital Sciences Center, Singapore. From Jan 2015 to June 2015, he visited UC Berkeley as a visiting scholar. His research interests include computer vision and machine learning. He has published more than 30 papers on object and face recognition related topics in many international conferences and journals, including IEEE T-PAMI,IJCV, IEEE TIP, IEEE TNNLS, IEEE TMM, IEEE TCSVT, CVPR, ECCV, etc. He has organized tutorials in VCIP2015 and ACCV2014. He was awarded the Microsoft Research Fellowship in 2010, and ACM Shanghai Young Research Scientist in 2015, and he is a recipient of National 1000 Young Talents Program in 2016.