Advertisement

Cataloging of Happy Facial Affect Using a Radial Basis Function Neural Network

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 221)

Abstract

The paper entitled “Cataloging of Happy facial Affect using a Radial Basis Function Neural Network” has developed an affect recognition system for identifying happy affect from faces using a RBF neural network. The methodology adapted by this research is a four step process: image preprocessing, marking of region of interest, feature extraction and a classification network. The emotion recognition system has been a momentous field in human–computer interaction. Though it is considerably a challenging field to make a system intelligent that is able to identify and understand human emotions for various vital purposes, e.g. security, society, entertainment but many research work has been done and going on, in order to produce an accurate and effective emotion recognition system. Emotion recognition system can be classified into facial emotion recognition and speech emotion recognition. This work is on facial emotion recognition that identifies one of the seven basic emotions i.e. happy affect. This is carried out by extracting unique facial expression feature; calculating euclidean distance, and building the feature vector. For classification radial basis function neural network is used. The deployment was done in Matlab. The happy affect recognition system gave satisfactory results.

Keywords

Image processing Radial basis function network Emotion recognition 

References

  1. 1.
    Harper R et al (2008) Being human: human-computer interaction in the year 2020. Microsoft Research Ltd.7JJ Thomson Avenue, Cambridge, CB3 0FB, EnglandGoogle Scholar
  2. 2.
    McAndrew A (2004) An introduction to digital image processing with matlab. Notes for SCM2511 image processing 1 semester 1. Victoria University of Technology, MelbourneGoogle Scholar
  3. 3.
    Arumugam D (2011) Emotion classification using facial expression. Int J Adv Comput Sci Appl (IJACSA) 2(7):92–98Google Scholar
  4. 4.
    Nagpal A, Garg A (2011) Recognition of expressions on human face using AI techniques. Int J Comput Sci Manage Stud (IJCSMS) 11(2):165–169Google Scholar
  5. 5.
    Sohail ASM, Bhattacharya P (2006) Detection of facial feature points using anthropometric face model. In: Proceedings fo IEEE international conference signal-image technology and internet-based systems, pp. 656–665Google Scholar
  6. 6.
    Bagherian E, Rahmat RW, Udzir NI (2009) Extract of facial feature point. IJCSNS Int J Comput Sci Netw Secur 9(1):49–53Google Scholar
  7. 7.
    Shan C et al (2009) Facial expression recognition based on statistical local features. Universal short title catalogue book, Chapter 4Google Scholar
  8. 8.
    Jansson M, Johansson J (2003) Interactive visualization of statistical data using multidimensional scaling techniques. Thesis, Institute of Technology, Linkopings University, SwedenGoogle Scholar
  9. 9.
    Praseeda LV, Sasikumar M (2008) A neural network based facial expression analysis using Gabor wavelets. World Acad Sci Eng Technol 42:563–567Google Scholar
  10. 10.
    Neggaz N, Besnassi M, Benyettou A (2010) Application of improved AAM and probabilistic neural network to facial expression recognition. J Appl Sci 10(15):1572–1579CrossRefGoogle Scholar
  11. 11.
    Hiremath V, Mayakar A (2009) Face recognition using Eigenface approach. In: IDT workshop on interesting results in computer science and engineering, SwedenGoogle Scholar
  12. 12.
    Fajaryanti J (2010) Implementation radial basis function neural network for training process in face recognition system. In: Proceedings of faculty of industrial technology. Gunadarma University, IndonesiaGoogle Scholar
  13. 13.
    Bors AG (1996) Introduction of the radial basis function (RBF) networks. In: Online Symposium of electronic engineersGoogle Scholar
  14. 14.
    Arulmozhi V (2011) Classification task by using matlab neural network tool box—a beginner’s view. Int J Wisdom Based Comput 1(2):59–60Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceChrist UniversityBangaloreINDIA

Personalised recommendations