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Emotion Estimation From Facial Images

BROWSE_DETAIL_CREATION_DATE: 22-02-2017

BROWSE_DETAIL_IDENTIFIER_SECTION

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BROWSE_DETAIL_PUBLISH_STATE: Unpublished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: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Najah, Goma Mohamed Salem (Author),

BROWSE_DETAIL_CONTRIBUTERS: Şengül, Gökhan (Advisor),

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Emotion estimation, Facial Expression Images, Expression Classification, Histogram of Oriented Gradient, Local Binary Pattern; K-Nearest Neighbors, Support Vector Machine.


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Prediction of emotions from facial images is one of the popular and active researches, and it’s implemented via many methods. In this thesis, the proposed system to predict emotions from facial expressions images contains several stages, first stage of this system is the pre-processing stage which is applied by detecting the face in images, then resizing the images, and then Histogram Equalization (HE) technique is applied to normalize the effects of illumination. The second stage is extracting features from facial expressions images using Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) feature extraction algorithms, which generates the training dataset and the testing dataset that contains expressions of Anger, Contempt, Disgust, Embarrass, Fear, Happy, Neutral, Pride, Sad, and Surprised. Then Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers are used for the classification stage in order to predict the emotion. In addition, Confusion Matrix (CM) technique is used to evaluate the performance of these classifiers. The proposed system is tested on JAFFE, KDEF, MUG, WSEFEP, TFEID and ADFES databases. However, the proposed system achieved prediction rate of 96.13% when HOG+SVM method is used.


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