Academy & Industry Research Collaboration Center (AIRCC)

Volume 10, Number 10, July 2020

Deep Learning Based Classification of 2D and 3D Images For
Facial Expression Recognition: Comparison Study


Fouzia Adjailia, Diana Olejarova and Peter Sincak, Technical University of Kosice, Slovak Republic


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.


Convolution neural network, facial expression classification, bosphorus, voxel classification.