Explicating ResNet for Facial Expression Recognition

Abstract

Convolution Neural Network is one of the phenomenal formulations in the field of pattern recognition research, computer vision and image processing. It helped to facilitate many theories into real working models. One of them being Facial expression recognition (FER) which has been benefited with the development of CNN architectures, especially the ResNet architecture which has emerged as the winner of ImageNet 2015 competition. Residual Neural Network (ResNet) inculcates the idea of skip connections and became the most cited neural network. In this paper we have analyzed an extensive view of addressing facial expression and emotion recognition with the assistance of ResNet. We have particularly emphasized upon Resnet-18, Resnet-50, SobelResNet, ResNet with Attention mechanism and deformable convolution, Emotion recognition using heart rate variability analysis and ResNet, 3D inception ResNet layers.