Abstract:
Humans communicate with one another by speaking,
gesticulating with their bodies, and expressing facial emotions.
Among these methods, expressing emotions play an important role.
Since human beings naturally use facial expressions to convey
their emotions. Micro-expressions are perceptive facial expressions
that last only a few seconds. Micro-expressions, as compared to
regular facial expressions, will expose the majority of the latent,
unconcealed emotional states. However, because of their shorter
length, micro-expressions are more difficult to find. As a result,
interest in micro-expression has grown in many fields, including
defence, psychology, and computer vision, in recent years. This
paper provides a brief overview of current methodologies for
detecting human micro-emotions, with a focus on the LBP, LBPTOP, DCNN, 3DHOG, MMPTR, and DTCM feature extraction
filter methods, which have been found to be more accurate. The
theoretical accuracy of the LBP-TOP Feature Extraction method
with SVM and KNN classifier combination was discovered to be
better than the theoretical accuracy of all approaches. As a result,
this paper also discusses those two classifiers.
