Abstract
In this paper, we proposed a new model for recognizing various emotions of humans with different age groups and gender. Fuzzy is used for extracting more accurate region of interest, i.e., face. The dimensionality of face image is reduced by the Principal Component Analysis (PCA) [12] and finally emotion is recognized and classified using Euclidean Distance. Database is prepared and some performance metrics like recognition-rate v/s Eigen-range has been calculated. The proposed method was also tested on FACES Collection database [13]. The experiment results demonstrate that the emotion recognition system has been successful with average recognition rate of 96.66% (with both experiment databases) when approximately or more than 60% eigenfaces used. It is also shown that database can be easily expanded to classify faces and non faces images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Deng, H.-B., Jin, L.-W., Zhen, L.-X., Huang, I.-C.: A New Facial Ex-pression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA. International Journal of Information Technology 11(11), 86–96 (2005)
Amir, J.: A Learning Fuzzy Model for Emotion Recognition. European Journal of Scientific Research 57(2), 206–211 (2011)
Kaur, M., Vashisht, R., Nirvair, N.: Recognition of Facial Expressions with Principal Component Analysis and Singular Value Decomposition. International Journal of Computer Applications 9(12), 36–40 (2010)
Kharat, G.U., Dudul, S.V.: Emotion Recognition from Facial Expression Using Neural Networks. In: HIS, Krakow, Poland, May 25-27. IEEE (2008)
Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE PAMI 12(1), 103–108 (1990)
Kishore, K.V.K., Varma, G.P.S.: Efficient Facial Emotion Classification with Wavelet Fusion of Multi Features. IJCSNS International Journal of Computer Science and Network Security 11(8) (2011)
Kosaka, Y., Kotani, K.: Facial Expression Analysis by Kernal Eigen Space Method based on Class Features (KEMC) Using Non-Linear Basis For Separation of Ex-pression Classes. In: International Conference on Image Processing, ICIP (2004)
Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A Optics and Image Science 4(3), 519–524 (1987)
Moriyama, T., Kanade, T., Xiao, J., Cohn, J.F.: Meticu-lously Detailed Eye region Model and It’s Application to Analysis of Facial Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5) (2006)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Yuille, A.L., Cohen, D.S., Hallinan, P.W.: Feature extraction from faces using deformable templates. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1989, June 4-8, pp. 104–109 (1989)
Dimitri, P.: Eigenface-based facial recognition (February 2003)
http://faces.mpdl.mpg.de/album/escidoc:57488 for downloading the FACE Collection database
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Tayal, S., Vijay, S. (2012). Human Emotion Recognition and Classification from Digital Colour Images Using Fuzzy and PCA Approach. In: Wyld, D., Zizka, J., Nagamalai, D. (eds) Advances in Computer Science, Engineering & Applications. Advances in Intelligent Systems and Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30111-7_100
Download citation
DOI: https://doi.org/10.1007/978-3-642-30111-7_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30110-0
Online ISBN: 978-3-642-30111-7
eBook Packages: EngineeringEngineering (R0)