Advertisement

Vowel Recognition from RGB-D Facial Information

  • José Carlos CastilloEmail author
  • Irene P. Encinar
  • Alfonso Conti-Morera
  • Álvaro Castro González
  • Miguel Ángel Salichs
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 476)

Abstract

One of the main concerns in developed countries is population ageing. Elder people are susceptible of suffering conditions which reduce quality of life such as apraxia of speech, a burden that requires prolongued therapy. Our proposal is intended to be a first step towards automated solutions that assist speech therapy through detecting mouth poses. This work proposes a system for vowel poses recognition from an RGB-D camera that provides 2D and 3D information. 2D data is fed into a face recognition approach able to accurately locate and characterize the mouth in the image space. The approach also uses 3D real world measures obtained after pairing the 2D detection with the 3D information. Both information sources are processed by a set of classifiers to ascertain the best option for vowel recognition.

Keywords

Apraxia of speech Visual recognition Classification RGB-D 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46(3), 175–185 (1992)MathSciNetGoogle Scholar
  2. 2.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers. Mach. Learn. 29(2–3), 131–163 (1997)CrossRefzbMATHGoogle Scholar
  3. 3.
    Gerstner, E., Lazar, R., Keller, C., Honig, L., Lazar, G.S., Marshall, R.: A Case of Progressive Apraxia of Speech in Pathologically Verified Alzheimer Disease. Cognitive & Behavioral Neurology 20(1), 15–20 (2007)CrossRefGoogle Scholar
  4. 4.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl 11(1), 10–18 (2009)CrossRefGoogle Scholar
  5. 5.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn., pp. 587–588. Springer (2008)Google Scholar
  6. 6.
    Holte, R.C.: Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Mach. Learn. 11(1), 63–90 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV, vol. 2, pp. 1150–1157 (1999)Google Scholar
  8. 8.
    Marti, P., Bacigalupo, M., Giusti, L., Mennecozzi, C., Shibata, T.: Socially assistive robotics in the treatment of behavioural and psychological symptoms of dementia. In: Biomedical Robotics and Biomechatronics, BioRob, pp. 483–488 (2006)Google Scholar
  9. 9.
    Milborrow, S., Nicolls, F.: Active Shape models with sift descriptors and mars. In: VISAPP, vol. 2, pp. 380–387 (2014)Google Scholar
  10. 10.
    Naruniec, J.: Discrete area filters in accurate detection of faces and facial features. Image and Vision Computing 32(12), 979–993 (2014)CrossRefGoogle Scholar
  11. 11.
    Olson, D.L., Delen, D.: Advanced data mining techniques. Springer Science & Business Media (2008)Google Scholar
  12. 12.
    Ozkan, S., Adapinar, D.O., Elmaci, N.T., Arslantas, D.: Apraxia for differentiating Alzheimers disease from subcortical vascular dementia and mild cognitive impairment. Neuropsychiatric Disease and Treatment 9, 947–951 (2013)CrossRefGoogle Scholar
  13. 13.
    Prabhu, U., Seshadri, K.: Facial Recognition Using Active Shape Models, Local Patches and Support Vector Machines (2009). http://www.contrib.andrew.cmu.edu/~kseshadr/ML_Paper.pdf (Accessed February 6, 2016)
  14. 14.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3(3.2), p. 5 (2009)Google Scholar
  15. 15.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
  16. 16.
    Rosenblatt, F.: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC (1961)zbMATHGoogle Scholar
  17. 17.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall (1995)Google Scholar
  18. 18.
    Schölkopf, B., Burges, C.J.C., Smola, A.J.: Advances in Kernel Methods: Support Vector Learning. MIT Press (1999)Google Scholar
  19. 19.
    Li, S.Z., Jain, A.K.: Handbook of Face Recognition. Springer-Verlag (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • José Carlos Castillo
    • 1
    Email author
  • Irene P. Encinar
    • 1
  • Alfonso Conti-Morera
    • 1
  • Álvaro Castro González
    • 1
  • Miguel Ángel Salichs
    • 1
  1. 1.RoboticslabUniversidad Carlos III de MadridMadridSpain

Personalised recommendations