Abstract
This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.
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Manduchi, R., Coughlan, J. (Computer) Vision without Sight. Commun. ACM 55(1), 96–104 (2012)
Krishna, S., Colbry, D., Black, J., Balasubramanian, V., Panchanathan, S., et al.: A Systematic Requirements Analysis and Development of an Assistive Device to Enhance the Social Interaction of People who are Blind or Visually Impaired. In: Workshop on Computer Vision Applications for the Visually Impaired (2008)
Knapp, M.L.: Nonverbal communication in human interaction. Cengage Learning (2012)
Wiener, W., Lawson, G.: Audition for the traveler who is visually impaired. Foundations of Orientation and Mobility 2, 104–169 (1997)
Dittmann, A.T., Llewellyn, L.G.: Relationship between vocalizations and head nods as listener responses. J. Pers. Soc. Psychol. 9(1), 79–84 (1968)
Choi, H., Rhee, P.: Head Gesture Recognition using HMMs. Expert Syst. Appl. 17(3), 213–221 (1999)
Kang, S.K., Chung, K.Y., Lee, J.H.: Development of head detection and tracking systems for visual surveillance. Pers. Ubiquit. Comput. 18(3), 515–522 (2014)
Kapoor, A., Picard, R.: A Real-Time Head Nod and Shake Detector. In: Workshop on Perceptive user interfaces, pp. 1–5. ACM (2001)
Tan, W., Rong, G.: A Real-Time Head Nod and Shake Detector using HMMs. Expert. Syst. Appl. 25(3), 461–466 (2003)
Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vision 4 (2001)
Gunes, H., Pantic, M.: Dimensional emotion prediction from spontaneous head gestures for interaction with sensitive artificial listeners. In: Allbeck, J.M., Badler, N.I., Bickmore, T.W., Pelachaud, C., Safonova, A. (eds.) IVA 2010. LNCS, vol. 6356, pp. 371–377. Springer, Heidelberg (2010)
Fujie, S., Ejiri, Y., Matsusaka, Y., Kikuchi, H., Kobayashi, T.: Recognition of para-linguistic information and its application to spoken dialogue system. In: Workshop on Automatic Speech Recognition and Understanding, pp. 231–236. IEEE (2003)
Wei, H., Scanlon, P., Li, Y., Monaghan, D.S., O’Connor, N.E.: Real-time head nod and shake detection for continuous human affect recognition. In: 14th International Workshop on Image Analysis for Multimedia Interactive Services, pp. 1–4. IEEE (2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Dementhon, D.F., Davis, L.S.: Model-based object pose in 25 lines of code. Int. J. Comput. Vision 15(1-2), 123–141 (1995)
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Trans. Pattern Anal. Machine Intell. 31(4), 607–626 (2009)
Xiong, X., De la Torre, F.: Supervised descent method and its applications to face alignment. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 532–539 (2013)
Martins, P., Batista, J.: Monocular head pose estimation. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 357–368. Springer, Heidelberg (2008)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
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Terven, J.R., Salas, J., Raducanu, B. (2014). Robust Head Gestures Recognition for Assistive Technology. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-Rodríguez, J., Suen, C.Y. (eds) Pattern Recognition. MCPR 2014. Lecture Notes in Computer Science, vol 8495. Springer, Cham. https://doi.org/10.1007/978-3-319-07491-7_16
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DOI: https://doi.org/10.1007/978-3-319-07491-7_16
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