  Facial expressions are important cues to observe human emotions. Facial
expression recognition has attracted many researchers for years, but it is
still a challenging topic since expression features vary greatly with the head
poses, environments, and variations in the different persons involved. In this
work, three major steps are involved to improve the performance of micro-facial
expression recognition. First, an Adaptive Homomorphic Filtering is used for
face detection and rotation rectification processes. Secondly, Micro-facial
features were used to extract the appearance variations of a testing
image-spatial analysis. The features of motion information are used for
expression recognition in a sequence of facial images. An effective
Micro-Facial Expression Based Deep-Rooted Learning (MFEDRL) classifier is
proposed in this paper to better recognize spontaneous micro-expressions by
learning parameters on the optimal features. This proposed method includes two
loss functions such as cross entropy loss function and centre loss function.
Then the performance of the algorithm will be evaluated using recognition rate
and false measures. Simulation results show that the predictive performance of
the proposed method outperforms that of the existing classifiers such as
Convolutional Neural Network (CNN), Deep Neural Network (DNN), Artificial
Neural Network (ANN), Support Vector Machine (SVM), and k-Nearest Neighbours
(KNN) in terms of accuracy and Mean Absolute Error (MAE).
