Takuya Maekawa
ABSTRACT
This paper presents novel unconstrained person identification for tabletop systems using a ceiling-mounted depth camera that overlooks a table. Recent state-of-the-art ubicomp, computer-vision, and CSCW studies have tried to recognize a user's activities and actions on a table using a ceiling-mounted device that overlooks the table. However, conventional unconstrained person identification methods such as face identification cannot be used for providing personalized services in such settings. In this study, we focus on a user's soft biometrics that can be captured from the ceiling such as the shoulder length, shape of the head, and posture of the back to achieve unconstrained person identification by using a ceiling-mounted depth camera. We achieve robust person identification by combining the soft biometrics within a framework of multiview learning. Multiview learning allows us to deal effectively with data consisting of features from multiple sources with different data distributions, i.e., multiple soft biometrics in our case. To the best of our knowledge, this is the first study that investigates the feasibility of person identification for tabletop users by a ceiling-mounted depth camera.
- Andrea F Abate, Michele Nappi, Daniel Riccio, and Gabriele Sabatino. 2007. 2D and 3D face recognition: A survey. Pattern Recognition Letters 28, 14 (2007), 1885--1906. Google Scholar
Digital Library
- V Ralph Algazi, Richard O Duda, Dennis M Thompson, and Carlos Avendano. 2001. The cipic hrtf database. In IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics. 99--102.Google ScholarCross Ref
- Ricardo M Araujo, Gustavo Graña, and Virginia Andersson. 2013. Towards skeleton biometric identification using the Microsoft Kinect sensor. In ACM SAC 2013. 21--26.Google ScholarDigital Library
- Thomas Augsten, Konstantin Kaefer, René Meusel, Caroline Fetzer, Dorian Kanitz, Thomas Stoff, Torsten Becker, Christian Holz, and Patrick Baudisch. 2010. Multitoe: high-precision interaction with back-projected floors based on high-resolution multi-touch input. In UIST 2010. 209--218. Google ScholarDigital Library
- Paul J Besl and Neil D McKay. 1992. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 2 (1992), 239--256. Google ScholarDigital Library
- Simone Bianco, Gianluigi Ciocca, and Paolo Napoletano. 2014. On the use of MKL for cooking action recognition. In IS&T/SPIE Electronic Imaging. 90240G.Google Scholar
- Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32. Google ScholarDigital Library
- Yizong Cheng. 1995. Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 8 (1995), 790--799. Google ScholarDigital Library
- Robert T Collins, Ralph Gross, and Jianbo Shi. 2002. Silhouette-based human identification from body shape and gait. In Fifth IEEE International Conference on Automatic Face and Gesture Recognition. 366--371.Google ScholarCross Ref
- Yujie Dong, Jenna Scisco, Mike Wilson, Eric Muth, and Andrew Hoover. 2014. Detecting periods of eating during free-living by tracking wrist motion. IEEE Journal of Biomedical and Health Informatics 18, 4 (2014), 1253--1260. Google Scholar
- Mehmet Gönen. 2012. Bayesian Efficient Multiple Kernel Learning. In ICML 2012.Google Scholar
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. Kernel Smoothing Methods. In The Elements of Statistical Learning. Springer, 191--218. Google ScholarCross Ref
- Stephen C Johnson. 1967. Hierarchical clustering schemes. Psychometrika 32, 3 (1967), 241--254.Google ScholarCross Ref
- Sönke Knoch, Frederic Kerber, Vladimir Pavlov, and Shreeraman Ponpathirkoottam. 2016. Automatic Capturing and Analysis of Manual Manufacturing Processes with Minimal Setup Effort. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp '16). 305--308. Google ScholarDigital Library
- Oliver Korn, Albrecht Schmidt, and Thomas Hörz. 2013. The potentials of in-situ-projection for augmented workplaces in production: a study with impaired persons. In CHI'13 Extended Abstracts. 979--984. Google ScholarDigital Library
- Daichi Kouno, Kazutaka Shimada, and Tsutomu Endo. 2012. Person identification using top-view image with depth information. In ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing. 140--145. Google ScholarDigital Library
- Vincent Lepetit, Pascal Lagger, and Pascal Fua. 2005. Randomized trees for real-time keypoint recognition. In CVPR 2005, Vol. 2. 775--781. Google ScholarDigital Library
- Roman Lissermann, Jochen Huber, Martin Schmitz, Jürgen Steimle, and Max Mühlhäuser. 2014. Permulin: mixed-focus collaboration on multi-view tabletops. In CHI 2014. 3191--3200. Google ScholarDigital Library
- Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan. 2009. uWave: Accelerometer-based personalized gesture recognition and its applications. In PerCom 2009. 1--9.Google Scholar
- Paul Luff, Marina Jirotka, Naomi Yamashita, Hideaki Kuzuoka, Christian Heath, and Grace Eden. 2013. Embedded interaction: The accomplishment of actions in everyday and video-mediated environments. ACM Transactions on Computer-Human Interaction (TOCHI) 20, 1 (2013), 6.Google ScholarDigital Library
- Paul Lukowicz, Jamie A Ward, Holger Junker, Mathias Stäger, Gerhard Tröster, Amin Atrash, and Thad Starner. 2004. Recognizing workshop activity using body worn microphones and accelerometers. In Pervasive 2004. 18--32. Google ScholarCross Ref
- Takuya Maekawa. 2013. A sensor device for automatic food lifelogging that is embedded in home ceiling light: A preliminary investigation. In PervasiveHealth Workshop on Lifelogging for Pervasive Health. Google ScholarDigital Library
- Takuya Maekawa, Daisuke Nakai, Kazuya Ohara, and Yasuo Namioka. 2016. Toward practical factory activity recognition: unsupervised understanding of repetitive assembly work in a factory. In UbiComp 2016. 1088--1099. Google ScholarDigital Library
- Takuya Maekawa and Shinji Watanabe. 2011. Unsupervised Activity Recognition with User's Physical Characteristics Data. In International Symposium on Wearable Computers (ISWC 2011). 89--96. Google ScholarDigital Library
- George Margetis, Dimitris Grammenos, Xenophon Zabulis, and Constantine Stephanidis. 2013. iEat: An Interactive Table for Restaurant Customers' Experience Enhancement. In HCI International 2013. 666--670.Google Scholar
- Margaret A McDowell, National Center for Health Statistics (US), and others. 2008. Anthropometric reference data for children and adults: United States, 2003--2006. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.Google Scholar
- Dan Pelleg and Andrew Moore. 2000. X-means: Extending k-means with efficient estimation of the number of clusters. In ICML 2000, Vol. 1. 727--734.Google Scholar
- Matthai Philipose, Kenneth P Fishkin, Mike Perkowitz, Donald J Patterson, Dieter Fox, Henry Kautz, and Dirk Hähnel. 2004. Inferring activities from interactions with objects. IEEE Pervasive Computing 3, 4 (2004), 50--57. Google ScholarDigital Library
- Johannes Preis, Moritz Kessel, Martin Werner, and Claudia Linnhoff-Popien. 2012. Gait recognition with Kinect. In 1st International Workshop on Kinect in Pervasive Computing.Google Scholar
- Stephan Richter, Christian Holz, and Patrick Baudisch. 2012. Bootstrapper: recognizing tabletop users by their shoes. In CHI 2012. 1249--1252. Google ScholarDigital Library
- Marcus Rohrbach, Sikandar Amin, Mykhaylo Andriluka, and Bernt Schiele. 2012. A database for fine grained activity detection of cooking activities. In CVPR 2012. 1194--1201. Google ScholarCross Ref
- Volker Roth, Philipp Schmidt, and Benjamin Güldenring. 2010. The IR ring: authenticating users' touches on a multi-touch display. In UIST 2010. 259--262.Google ScholarDigital Library
- Dominik Schmidt, Ming Ki Chong, and Hans Gellersen. 2010. HandsDown: hand-contour-based user identification for interactive surfaces. In The 6th Nordic Conference on Human-Computer Interaction. 432--441. Google ScholarDigital Library
- Yifan Shi, Yan Huang, David Minnen, Aaron Bobick, and Irfan Essa. 2004. Propagation networks for recognition of partially ordered sequential action. In CVPR 2004, Vol. 2. 862--869.Google Scholar
- Jamie Shotton, Toby Sharp, Alex Kipman, Andrew Fitzgibbon, Mark Finocchio, Andrew Blake, Mat Cook, and Richard Moore. 2013. Real-time human pose recognition in parts from single depth images. Commun. ACM 56, 1 (2013), 116--124. Google ScholarDigital Library
- Vijay Srinivasan, John Stankovic, and Kamin Whitehouse. 2010. Using height sensors for biometric identification in multi-resident homes. In Pervasive2010. 337--354. Google ScholarDigital Library
- Sebastian Stein and Stephen J McKenna. 2013. Combining embedded accelerometers with computer vision for recognizing food preparation activities. In UbiComp 2013. 729--738.Google Scholar
- Atsushi Sugiura and Yoshiyuki Koseki. 1998. A user interface using fingerprint recognition: holding commands and data objects on fingers. In UIST 98. 71--79. Google ScholarDigital Library
- Liang Wang, Tieniu Tan, Huazhong Ning, and Weiming Hu. 2003. Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 12 (2003), 1505--1518. Google ScholarDigital Library
- Jianxin Wu, Adebola Osuntogun, Tanzeem Choudhury, Matthai Philipose, and James M Rehg. 2007. A scalable approach to activity recognition based on object use. In ICCV 2007. 1--8.Google ScholarCross Ref
Index Terms
- Identification from ceiling: unconstrained person identification for tabletops using multiview learning
Recommendations
Dynamics Analysis of Facial Expressions for Person Identification
CAIP '09: Proceedings of the 13th International Conference on Computer Analysis of Images and PatternsWe propose a new method for analyzing the dynamics of facial expressions to identify persons using Active Appearance Models and accurate facial feature point tracking. Several methods have been proposed to identify persons using facial images. In most ...
Face Recognition Based Person Specific Identification for Video Surveillance Applications
WCI '15: Proceedings of the Third International Symposium on Women in Computing and InformaticsFace detection is an important aspect for applications like biometrics, video surveillance and human computer interaction. Videos provide abundant information and also that can be leveraged by temporal variations in pose, expression changes and ...
Person Identification Using Top-View Image with Depth Information
SNPD '12: Proceedings of the 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed ComputingIn this paper, we describe a novel image-based person identification task. Traditional face-based person identification methods have a low tolerance for occluded situation, such as overlapping of people in an image. We focus on an image from an overhead ...
Comments