Silvia Rus
Arjan Kuijper
ABSTRACT
Currently there is much research in the area of person identification. Mostly it is based on multi-biometric data. In this paper, we aim to leverage soft biometric properties to achieve person re-identification by using unobtrusive sensors, envisioning assistive environments, which recognize their user and thus automatically personalize and adapt. In practice, a car seat recognizes the person who sits down and greets the person with their own name, enabling various customisation in the car unique to the user, like seat configurations.
We present a system composed of a sensor equipped car seat, which is able to recognize a person from a predefined group. We contribute two classification approaches based on cosine similarity measure and on triplet loss learning. These are thoroughly analysed and evaluated in a user study with nine participants. We achieve the best re-identification performance using a hand-crafted feature approach based on the comparing measure of cosine similarity combined with majority voting. The highest overall precision achieved in re-identifying a person from a group of nine users is 80 %.
- Dan Boneh and Matt Franklin. 2001. Identity-Based Encryption from the Weil Pairing. In Advances in Cryptology — CRYPTO 2001, Joe Kilian (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 213–229.Google Scholar
Digital Library
- Yuri Boykov and Gareth Funka-Lea. 2006. Graph cuts and efficient ND image segmentation. International journal of computer vision 70, 2 (2006), 109–131.Google Scholar
- Andreas Braun, Sebastian Frank, Martin Majewski, and Xiaofeng Wang. 2015. CapSeat: Capacitive Proximity Sensing for Automotive Activity Recognition(AutomotiveUI ’15). Association for Computing Machinery, New York, NY, USA, 225–232. https://doi.org/10.1145/2799250.2799263Google ScholarDigital Library
- Andreas Braun, Ingrid Schembri, and Sebastian Frank. 2015. ExerSeat - Sensor-Supported Exercise System for Ergonomic Microbreaks. In Ambient Intelligence, Boris De Ruyter, Achilles Kameas, Periklis Chatzimisios, and Irene Mavrommati (Eds.). Springer International Publishing, Cham, 236–251.Google Scholar
- George H Dunteman. 1989. Basic concepts of principal components analysis. , 15–22 pages.Google Scholar
- Sebastian Frank. 2014. Capacitive Proximity Sensing Supported Advanced Driver Assistance System.Google Scholar
- Tobias Grosse-Puppendahl, Xavier Dellangnol, Christian Hatzfeld, Biying Fu, Mario Kupnik, Arjan Kuijper, Matthias R. Hastall, James Scott, and Marco Gruteser. 2016. Platypus: Indoor Localization and Identification through Sensing of Electric Potential Changes in Human Bodies. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (Singapore, Singapore) (MobiSys ’16). Association for Computing Machinery, New York, NY, USA, 17–30. https://doi.org/10.1145/2906388.2906402Google ScholarDigital Library
- Gierad Laput, Chouchang Yang, Robert Xiao, Alanson Sample, and Chris Harrison. 2015. EM-Sense: Touch Recognition of Uninstrumented, Electrical and Electromechanical Objects(UIST ’15). Association for Computing Machinery, New York, NY, USA, 157–166. https://doi.org/10.1145/2807442.2807481Google ScholarDigital Library
- Carl A Pickering. 2008. Human vehicle interaction based on electric field sensing. In Advanced Microsystems for Automotive Applications 2008. Springer, 141–154.Google ScholarCross Ref
- Stuart Russell and Peter Norvig. 2002. Artificial intelligence: a modern approach. (2002).Google Scholar
- Munehiko Sato, Ivan Poupyrev, and Chris Harrison. 2012. Touché: enhancing touch interaction on humans, screens, liquids, and everyday objects. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 483–492.Google ScholarDigital Library
- Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition. 815–823.Google ScholarCross Ref
- Joshua Reynolds Smith. 1999. Electric field imaging. Ph.D. Dissertation. Massachusetts Institute of Technology.Google ScholarDigital Library
- H. Z. Tan, L. A. Slivovsky, and A. Pentland. 2001. A sensing chair using pressure distribution sensors. IEEE/ASME Transactions on Mechatronics 6, 3 (Sep. 2001), 261–268. https://doi.org/10.1109/3516.951364Google ScholarCross Ref
- Matthew Turk and Alex Pentland. 1991. Eigenfaces for recognition. Journal of cognitive neuroscience 3, 1 (1991), 71–86.Google ScholarDigital Library
- Liang Zheng, Yi Yang, and Alexander G. Hauptmann. 2016. Person Re-identification: Past, Present and Future. arxiv:1610.02984 [cs.CV]Google Scholar
Recommendations
Learning similarity with cosine similarity ensemble
This paper proposes a cosine similarity ensemble (CSE) method to learn similarity.CSE is a selective ensemble and combines multiple cosine similarity learners.A learner redefines the pattern vectors and determines its threshold adaptively.Experimental ...
A driver’s car-following behavior prediction model based on multi-sensors data
AbstractThe prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to ...
Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification
Pattern Recognition. ICPR International Workshops and ChallengesAbstractThe latest generation cars are often equipped with advanced driver assistance systems, usually known as ADAS (Advanced Driver Assistance Systems). These systems are able to assist the car driver by leveraging several levels of automation. ...
Comments