Facial Expression Recognition

The purpose of this project was to analyze which image pre-processing technique was most beneficial in improving the performance of Facial Expression Recognition through Deep Learning and High-Performance Computing. Contrary to our expectations, the results obtained in this work showed that deep learning does not significantly benefit from various commonly used image pre-processing techniques such as resizing, smoothing, or edge detection. The results confirm previous findings that an increase in accuracy is obtained by increasing the size of the training dataset. This study proceeds to show that the increase in training data size can easily be handled by the High-Performance Computing (HPC) cluster provided by the Pittsburg Supercomputing Center (PSC) through XSEDE.