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DOI: 10.14569/IJACSA.2011.020117
PDF

Automatic Facial Feature Extraction and Expression Recognition based on Neural Network

Author 1: S P Khandait
Author 2: Dr. R.C.Thool
Author 3: P.D.Khandait

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 2 Issue 1, 2011.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.

Keywords: Edge projection analysis, Facial features, feature extraction, feed forward neural network, segmentation SUSAN edge detection operator.

S P Khandait, Dr. R.C.Thool and P.D.Khandait, “ Automatic Facial Feature Extraction and Expression Recognition based on Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 2(1), 2011. http://dx.doi.org/10.14569/IJACSA.2011.020117

@article{Khandait2011,
title = { Automatic Facial Feature Extraction and Expression Recognition based on Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.020117},
url = {http://dx.doi.org/10.14569/IJACSA.2011.020117},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {1},
author = {S P Khandait and Dr. R.C.Thool and P.D.Khandait}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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