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14 septembre 2016
- handle: 10670/1.f8nnj6
- hal: hal-01637498
- doi: 10.1007/978-3-319-45378-1_55
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-45378-1_55
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
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Face expression Feature extraction Feature encoding Slant tracking Artificial Neural NetworksSujets proches
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Majida Albakoor et al., « FE8R - A Universal Method for Face Expression Recognition », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-319-45378-1_55
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Résumé
This paper proposes a new method for recognition of face expressions, called FE8R. We studied 6 standard expressions: anger, disgust, fear, happiness, sadness, surprise, and additional two: cry and natural. For experimental evaluation samples from MUG Facial Expression Database and color FERET Database were taken, with addition of cry expression. The proposed method is based on the extraction of characteristic objects from images by gradient transformation depending on the coordinates of the minimum and maximum points in each object on the face area. The gradient is ranked in $$[-15,+35]$$ degrees. Essential objects are studied in two ways: the first way incorporates slant tracking, the second is based on feature encoding using BPCC algorithm with classification by Backpropagation Artificial Neural Networks. The achieved classification rates have reached 95 %. The second method is proved to be fast and producing satisfactory results, as compared to other approaches.