A genetic programming-based feature selection and fusion for facial expression recognition
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- @Article{GHAZOUANI:2021:ASC,
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author = "Haythem Ghazouani",
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title = "A genetic programming-based feature selection and
fusion for facial expression recognition",
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journal = "Applied Soft Computing",
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volume = "103",
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pages = "107173",
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year = "2021",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2021.107173",
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URL = "https://www.sciencedirect.com/science/article/pii/S156849462100096X",
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keywords = "genetic algorithms, genetic programming, Facial
expression recognition, Feature selection, Feature
fusion, Geometric feature, Texture feature",
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abstract = "Emotion recognition has become one of the most active
research areas in pattern recognition due to the
emergence of human-machine interaction systems.
Describing facial expression is a very challenging
problem since it relies on the quality of the face
representation. A multitude of features have been
proposed in the literature to describe facial
expression. None of these features is universal for
accurately capturing all the emotions since facial
expressions vary according to the person, gender and
type of emotion (posed or spontaneous). Therefore, some
research works have considered combining several
features to enhance the recognition rate. But they
faced significant problems because of information
redundancy and high dimensionality of the resulting
features. In this work, we propose a genetic
programming framework for feature selection and fusion
for facial expression recognition, which we called
GP-FER. The main component of this framework is a
tree-based genetic program with a three functional
layers (feature selection, feature fusion and
classification). The proposed genetic program is a
binary classifier that performs discriminative feature
selection and fusion differently for each pair of
expression classes. The final emotion is captured by
performing a unique tournament elimination between all
the classes using the binary programs. Three different
geometric and texture features were fused using the
proposed GP-FER. The obtained results, on four posed
and spontaneous facial expression datasets (DISFA,
DISFA+, CK+ and MUG), show that the proposed facial
expression recognition method has outperformed, or
achieved a comparable performance to the
state-of-the-art methods",
- }
Genetic Programming entries for
Haythem Ghazouani
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