Abstract
This paper provides a specifically adapted methodology for supporting the audiologists when testing the hearing of patients with cognitive decline or other communication disabilities. These patients can not interact with the audiologist conventionally, but they often express gestural reactions when they perceive the auditory stimuli typically associated to the eyes region. From a video sequence captured during the hearing evaluation, we analyze the movements in the area of the patient’s eyes, so we can detect these gestural reactions. We define a set of different gestures for classification, based on the expert knowledge. The proposed method achieves an accuracy of the 90.65% when classifying these movements, showing their separability, and therefore, the possibility of interpreting them with high-level information as positive reactions to the auditory stimuli.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Davis, A.: The prevalence of hearing impairment and reported hearing disability among adults in great britain. Int. J. Epidemiol. 18, 911–917 (1989)
Espmark, A., Scherman, M.: Hearing confirms existence and identity-experiences from persons with presbyacusis. Int. J. Audio 42, 106–115 (2003)
Fernández, A., Ortega, M., Cancela, B., Penedo, M., Vazquez, C., Gigirey, L.: Automatic processing of audiometry sequences for objective screening of hearing loss. Expert Syst. Appl. 39(16), 12683–12696 (2012)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the IIJCAI 1981, pp. 674–679 (1981)
Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput Vision 57, 137–154 (2004)
Geetha, A., Ramalingam, V., Palanivel, S., Palaniappan, B.: Facial expression recognition - a real time approach. Expert Syst. Appl. 36(1), 303–308 (2009)
Kumano, S., Otsuka, K., Yamato, J., Maeda, E., Sato, Y.: Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates. Int. J. Comput. Vision 83(2), 178–194 (2009)
Akakin, H.C., Sankur, B.: Robust classification of face and head gestures in video. Image Vision Comput. 29(7), 470–483 (2011)
Dibeklioglu, H., Ortega, M., Kosunen, I., Zuzanek, P., Salah, A., Gevers, T.: Design and implementation of an affect-responsive interactive photo frame. Journal on Multimodal User Interfaces 4, 81–95 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fernandez, A., Ortega, M., Penedo, M.G., Cancela, B., Gigirey, L.M. (2013). Automatic Eye Gesture Recognition in Audiometries for Patients with Cognitive Decline. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-39094-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39093-7
Online ISBN: 978-3-642-39094-4
eBook Packages: Computer ScienceComputer Science (R0)