Face Detection and Person Identification on Mobile Platforms
The Desire project aimed at the development and implementation of a mobile service robotic research platform (technology platform) able to handle real world scenarios regarding service robotic tasks. Different modules for different tasks plus an interaction infrastructure were integrated on this platform. An example of a real world scenario task is the support of a handicapped person to clean up a kitchen in home environments.
One of the main challenges to be solved in this field is the interaction with people. To start an interaction process between a robot and a person, the most important information is the knowledge about the interacting partner’s identity and whether the interacting partner is present or not. This means, the robot must be able to detect and be finally able to identify persons. Accurate identification of specific individuals has to be done by analyzing the individual features of each person. A typical feature set that allows for a distinct identification of a specific person is often extracted from the facial image acquired by a camera. This feature-set is stored in a database to allow the identification of different persons independent from place and time by comparing given feature-sets. Thus, a face recognition module was integrated into the technology platform which includes face detection and identification algorithms.
KeywordsFace Detection Mobile Platform Technology Platform Service Robot Laser Range Finder
Unable to display preview. Download preview PDF.
- 1.Arras, K.O., Martinez-Mozos, O. (eds.): ICRA 2009 Workshop Proceedings of People Detection and Tracking (2009)Google Scholar
- 2.Arras, K.O., Lau, B., Grzonka, S., Luber, M., Mozos, O.M., Meyer-Delius, D., Burgard, W.: Range-Based People Detection and Tracking for Socially Enabled Service Robots. In: Prassler, E., et al. (eds.) Towards Service Robots for Everyday Environ. STAR, vol. 76, pp. 235–280. Springer, Heidelberg (2012)Google Scholar
- 3.Gehlen, S., Rinne, M., Werner, M.: Hierarchical Graph-Matching, European Patent 01118536.0 (2001)Google Scholar
- 4.Jonathon Phillips, P., Todd Scruggs, W., O’Toole, A.J., Flynn, P.J., Bowyer, K.W., Schott, C.L., Sharpe, M.: FRVT 2006 and ICE 2006 Large-Scale Results; National Institute of Standards and Technology Gaithersburg, MD 20899; NISTIR 7408 (March 2007)Google Scholar
- 5.Rowley, H.A., Baluja, S., Kanade, T.: Neural Network-Based Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1) (January 1998)Google Scholar
- 6.Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2) (2004)Google Scholar
- 7.Viola, P., Jones, M.J.: Fast Multi-view Face Detection, Mitsubishi Electric Research Laboratories, TR2003-096 (August 2003)Google Scholar
- 8.Wichert, G.v., Klimowicz, C., Neubauer, W., Wösch, T., Lawitzky, G., Caspari, R., Heger, H.-J., Witschel, P., Handmann, U., Rinne, M.: The Robotic Bar – An Integrated Demonstration of a Robotic Assistant. In: Advances in Human-Robot Interaction. Springer Tracts in Advanced Robotics (STAR), vol. 14. Springer, Heidelberg (2004) ISBN: 3-540-23211-7Google Scholar
- 11.Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1) (January 2002)Google Scholar