Facial expressions are an important communication channel among human beings. The Classification of facial expressions is a research area which has been proposed in several fields in recent years, it provides insight into how human can express their emotions which can be used to inform and identify a person's emotional state. In this paper, we provide the basic outlines of both two dimensional and three-dimensional facial expression classification with a number of concepts in detail and the extent of their influence on the classification process. We also compare the accuracy of two-dimensional (2D) and three-dimensional (3D) proposed models to analyse the 2D and 3D classification using comprehensive algorithms based on convolution neural network, the model was trained using a commonly used dataset named Bosphorus. Using the same experimental setup, we discussed the results obtained in terms of accuracy and set a new challenge in the classification of facial expression.
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