Abstract
We have developed an autonomous mobile robot which monitors the face of a desk worker. The robot uses three kinds of information observed with its Kinect to search for the desk worker and adjusts its position for monitoring. The monitoring is based on incremental clustering of the faces. Our experiments revealed that not only Animation Units (AUs) features, which represent deviations from the neutral face, but also the pitch angle of the face normalized in a new way are necessary for a valid clustering under specific conditions. Our robot lost sight of a desk worker only once in experiments for 8 persons for about 50 minutes. The resulting clusters correspond to “yawning”, “smiling”, and “reading” for a half of the desk workers with high NMI (normalized mutual information), which is an evaluation measure often used in clustering.
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References
Aarts, E., Encarnacao, J.: Into Ambient Intelligence. In: True Vision - The Emergence of Ambient Intelligence, pp. 1–16. Springer-Verlag (2006)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A Framework for Clustering Evolving Data Streams. In: Proc. VLDB 2003, pp. 81–92 (2003)
Ahlberg, J.: Candide-3 An Updated Parameterised Face. Technical Report LiTH-ISY-R-2326, Dept. Elec. Eng., Linköping University Sweden (2001)
Coradeschi, S., et al.: GiraffPlus: Combining Social Interaction and Long Term Monitoring for Promoting Independent Living. In: Proc. HSI, pp. 6–15 (2013)
Deguchi, Y., Suzuki, E.: Skeleton Clustering by Autonomous Mobile Robots for Subtle Fall Risk Discovery. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS, vol. 8502, pp. 500–505. Springer, Heidelberg (2014)
Deguchi, Y., Takayama, D., Takano, S., Scuturici, V.-M., Petit, J.-M., Suzuki, E.: Multiple-Robot Monitoring System Based on a Service-Oriented DBMS. In: Proc. Seventh ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2014) (2014)
Erna, A., Yu, L., Zhao, K., Chen, W., Suzuki, E.: Facial Expression Data Constructed with Kinect and Their Clustering Stability. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, Y.-S. (eds.) AMT 2014. LNCS, vol. 8610, pp. 421–431. Springer, Heidelberg (2014)
Fischinger, D., et al.: HOBBIT - The Mutual Care Robot. In: Proc. ASROB (2013)
Gripay, Y., Laforest, F., Petit, J.-M.: A Simple (yet Powerful) Algebra for Pervasive Environments. In: Proc. EDBT, pp. 359–370 (2010)
Hossny, M., et al.: Low Cost Multimodal Facial Recognition via Kinect Sensors. CISR, Deakin University, Australia, Technical report (2013)
Kranen, P., Assent, I., Baldauf, C., Seidl, T.: The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining. Knowledge and Information Systems 29(2), 249–272 (2011)
Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding Facial Expressions with Gabor Wavelets. In: Proc. Third International Conference on Face and Gesture Recognition (FG), pp. 200–205 (1998)
Rubenstein, L.Z.: Falls in Older People: Epidemiology, Risk Factors and Strategies for Prevention. Age and Ageing, ii37–ii41 (2006)
Ruvolo, P., Eaton, E.: ELLA: An Efficient Lifelong Learning Algorithm. In: Proc. ICML 2013, vol. 1, pp. 507–515 (2013)
Sebe, N., Lew, M., Sun, Y., Cohen, I., Geners, T., Huang, T.: Authentic Facial Expression Analysis. Image and Vision Computing 25, 1856–1863 (2007)
Seidl, T., Assent, I., Kranen, P., Krieger, R., Herrmann, J.: Indexing Density Models for Incremental Learning and Anytime Classification on Data Streams. In: Proc. EDBT 2009, pp. 311–322 (2009)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-Time Human Pose Recognition in Parts from Single Depth Images. In: Proc. CVPR 2011, pp. 1297–1304 (2011)
Suzuki, E., Deguchi, Y., Takayama, D., Takano, S., Scuturici, V.-M., Petit, J.-M.: Towards Facilitating the Development of Monitoring Systems with Low-Cost Autonomous Mobile Robots. In: Kawtrakul, A., Laurent, D., Spyratos, N., Tanaka, Y. (eds.) ISIP 2013. CCIS, vol. 421, pp. 57–70. Springer, Heidelberg (2014)
Valstar, M.F., Jiang, B., Mehu, M., Pantic, M., Schrer, K.: The First Facial Expression Recognition and Analysis Challenge. In: Proc. International Conference on Face and Gesture Recognition (FG), pp. 921–926 (2011)
Verma, A., Sharma, L.K.: A Comprehensive Survey on Human Facial Expression Detection. Int’l J. Image Processing 7(2), 171–182 (2013)
Yong, C., Sudirman, R., Chew, K.: Facial Expression Monitoring System Using PCA-Bayes Classifier. In: Future Computer Sciences and Application (ICFCSA), pp. 187–191 (2011)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: A New Data Clustering Algorithm and its Applications. Data Mining and Knowledge Discovery 1(2), 141–182 (1997)
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Kondo, R., Deguchi, Y., Suzuki, E. (2014). Developing a Face Monitoring Robot for a Desk Worker. In: Aarts, E., et al. Ambient Intelligence. AmI 2014. Lecture Notes in Computer Science(), vol 8850. Springer, Cham. https://doi.org/10.1007/978-3-319-14112-1_19
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DOI: https://doi.org/10.1007/978-3-319-14112-1_19
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