Facial Expression Recognition Techniques and Comparative Analysis Using Classification Algorithms
Abstract
With the development of technology and hardware possibilities, it has become
possible to analyze the changes that occur as a result of the reflection of emotional
state on facial expression with computer vision applications. Facial expression
analysis systems are used in applications such as security systems, early diagnosis of
some diseases in the field of medicine, human-computer interaction, and safe driving.
Facial expression analysis systems developed using image data consist of 3 basic
stages. These are; extracting the face area from the input data, extracting the feature
vectors of the data and classifying the feature vectors. In this study, a hybrid method
for facial expression analysis is proposed. The method aims to combine the ability of
deep learning models in feature extraction with the ability of machine learning to
classify small datasets. Multi Task Cascaded Convolutional Network (MTCNN) has
been used to detect the face region in the input data. The features extracted from the
fully connected layer of the AlexNet model, which achieves successful results in
classification problems, have been classified with K-Nearest Neighborhood (KNN),
Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA)
algorithms. Machine learning and deep learning methods are widely used in facial
expression analysis systems proposed in the literature. In this study, the performances
of LDA, SVM and KNN algorithms have been analyzed using JAFFE dataset without
data augmentation. With LDA, SVM and KNN algorithms, 89.2%, 88.3% and 87.8%
accuracy has been achieved respectively.
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