The subject of emotion detection from digital image
has gained exceptional importance in recent years due to the
expansion of visual applications in variowefields of life. With
respect to the emotion of human face, the matter becomes more
complex according to its variety. At the same time, this matter
becomes easier if the computerized technique is used to learn
most known emotions and then detect it in the final imaging
system. In this paper, a new approach for detecting emotion of
human face has been proposed using artificial neural network
(ANN). This network is feed by a set of histogram of gradient
(HOG) features, as a representative reference to describe the
entire emotion. The determining of HOG features is limited to
specific region of the face within the digital image. This region is
designed to take T shape which covers main parts of human face
(eye, noise, mouth, and eyebrow) that are changed with emotion
type. The proposed approach is evaluated by standard emotion
dataset (JAFFE) in both phases of ANN (training and testing).
The simulation results view significant percentage of accuracy in
comparison with the existing technique of emotion detection.