Analysis of Convolutional Neural Networks for Facial Expression Recognition on GPU, TPU and CPU

Abstract

In light of the increasing computational capacity provided by Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs), all of these were designed to speed up deep learning workloads, and the fact that this iteration of human-computer interaction is becoming more natural and social, it is clear that the field of human-computer interaction is poised for significant growth. The scientific community has found emotion recognition to be of tremendous interest and significance. Despite these advances, it is still desired that research into computational methods for identifying and recognizing emotions at the same ease as humans. This study uses Convolutional Neural Networks (CNN) for human emotion identification from facial expressions to delve deeper into this topic. The results demonstrated that training an Artificial Neural Networks (ANN) on GPUs might cut computational time by as much as 90% while accuracy could be raised up to 65%.