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Improved Facial Expression Recognition with Xception Deep Net and Preprocessed Images |
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PP: 859-865 |
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doi:10.18576/amis/130520
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Author(s) |
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Maksat Kanatov,
Lyazzat Atymtayeva,
Mateus Mendes,
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Abstract |
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| Automated Facial Expression Recognition (FER) is an important part of computer-human interaction. For decades, researchers and scientists have been trying to create a model of artificial intelligence that could think, learn, make decisions and act in a way similar to a real person. Among other skills, such model needs to recognise human facial expression to understand non-verbal language. The present paper describes a method to fine tune the FER process in images, using deep learning CNN model Xception, with preprocessing the images. The method has shown improved results when applied to different datasets.
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