Publications

Abstract : Facial expression for emotion detection has taken wide popularity with visible images using machine learning techniques and convolutional neural networks. However, emotion recognition from visible images is not much plausible as they are sensitive to light conditions and people can easily fake expression. In this paper, we propose a method for facial expression recognition with thermal images using ResNet152. Residual networks are easier to optimize, and can gain accuracy from considerably increased depth. The objective of this paper is to use a pre-trained modified ResNet152 to train thermal facial images in order to predict different emotions. We use natural visible and infrared facial expression (NVIE) dataset for emotion classification.