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FACIAL EXPRESSION IDENTIFICATION USING TEXTURE AND SHAPE BASED FEATURES

BROWSE_DETAIL_CREATION_DATE: 20-12-2016

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: SCIENCE, TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Gül, Nuray (Author),

BROWSE_DETAIL_CONTRIBUTERS: Tora, Hakan (Advisor),

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Facial expression recognition systems, emotion identification, human-computer interaction, extended Cohn-Kanade Dataset (CK+), neural network, Fourier Descriptors, lip boundary, Bezier Curves, local binary patterns


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Recently, facial expression recognition (FER) systems have a significant role to play in the human-computer interaction (HCI) applications. In many existing systems, either the features of the whole face or the combination of the features extracted from some regions of face are used while defining an emotion. This study suggests using just one appropriate region for every single expression identification to demonstrate what is the effect of these regions on the feelings separately. In the proposed design, it’s aimed to identify Surprised and Happy emotions by using shape features of mouth region on the other hand the texture features of the eye region is used for Fear, Anger and Disgust emotions. Therefore, Fourier Descriptors (FD) and Local Binary Patterns (LBP) are extracted as feature vectors and these features are classified by using neural networks (NN). The system was trained on the Extended Cohn-Kanade Dataset (CK+) and achieved accuracy rate is almost 88.9% for the overall system


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