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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique

Author 1: Jeemoni Kalita Author 2: Karen Das
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 4, No. 2 · Published 2013 · Cited by 8

DOI: https://doi.org/10.14569/IJACSA.2013.040229

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Abstract

In this paper, an Eigenvector based system has been presented to recognize facial expressions from digital facial images. In the approach, firstly the images were acquired and cropping of five significant portions from the image was performed to extract and store the Eigenvectors specific to the expressions. The Eigenvectors for the test images were also computed, and finally the input facial image was recognized when similarity was obtained by calculating the minimum Euclidean distance between the test image and the different expressions.

Keywords

Facial expression recognition facial expressions Eigenvectors Eigenvalues

How to Cite this Article

Kalita, J., & Das, K. (2013). Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique. International Journal of Advanced Computer Science and Applications, 4(2). https://doi.org/10.14569/IJACSA.2013.040229

Kalita, Jeemoni, and Karen Das. "Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique." International Journal of Advanced Computer Science and Applications, vol. 4, no. 2, 2013, https://doi.org/10.14569/IJACSA.2013.040229.

@article{Kalita2013,
  title     = {Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {2},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Jeemoni Kalita and Karen Das},
  doi       = {10.14569/IJACSA.2013.040229},
  url       = {https://doi.org/10.14569/IJACSA.2013.040229}
}

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