Author : NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors : Yomna M. Elbarawy,Rania Salah El-Sayed
Source : International Journal of Image, Graphics and Signal Processing
Date of Publication : 10/2019
Abstract :
Facial expressions are undoubtedly the best
way to express human attitude which is crucial in social
communications. This paper gives attention for exploring
the human sentimental state in thermal images through
Facial Expression Recognition (FER) by utilizing
Convolutional Neural Network (CNN). Most traditional
approaches largely depend on feature extraction and
classification methods with a big pre-processing level but
CNN as a type of deep learning methods, can
automatically learn and distinguish influential features
from the raw data of images through its own multiple
layers. Obtained experimental results over the IRIS
database show that the use of CNN architecture has a
96.7% recognition rate which is high compared with
Neural Networks (NN), Autoencoder (AE) and other
traditional recognition methods as Local Standard
Deviation (LSD), Principle Component Analysis (PCA)
and K-Nearest Neighbor (KNN)
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