%0 Journal Article %1 IJACSA.2011.020117 %A S P Khandait Dr. R.C.Thool, P.D.Khandait %D 2011 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K Facial SUSAN analysis detection edge extraction, feature features, feed forward network, neural operator.,Edge projection segmentation %N 1 %T Automatic Facial Feature Extraction and Expression Recognition based on Neural Network %U http://ijacsa.thesai.org/ %V 2 %X 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.

@article{IJACSA.2011.020117, 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. }, added-at = {2014-02-21T08:00:08.000+0100}, author = {{S P Khandait Dr. R.C.Thool}, P.D.Khandait}, biburl = {https://www.bibsonomy.org/bibtex/29c7f1ebf45c5a8125c541bec6bb3343e/thesaiorg}, interhash = {391d5b65590e38e8c41ee142978ef8fe}, intrahash = {9c7f1ebf45c5a8125c541bec6bb3343e}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {Facial SUSAN analysis detection edge extraction, feature features, feed forward network, neural operator.,Edge projection segmentation}, number = 1, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Automatic Facial Feature Extraction and Expression Recognition based on Neural Network}}, url = {http://ijacsa.thesai.org/}, volume = 2, year = 2011 }