A Bird View on Deep Learning Facial Expression Recognition Approaches for Thermal and Infrared Images - Nehlani, Reshma; Pandya, Devang (International Journal of Computer Sciences and Engineering, 2018)
With the capability to self-learn and succeed to achieve favourable results in various classification problem, deep learning techniques are increasingly used for Automatic Facial Expression Recognition (AFER). In this paper, we provide brief survey on deep learning technique particularly Convolutional Neural Networks (CNN) for Facial Expression Recognition (FER) and newly introduced Infrared based FER dataset. This review is focused on various CNN techniques applied in almost last half decade for FER on Infrared and Visible light images. There are certain unique advantages of using thermal and infrared images which can make FER techniques robust. Paper describes the standard flow of deep facial expression recognition and suggested methods based on research conducted specifically in this area. Later, review of existing novel deep neural networks and implementations for still images and video-based FER for Infrared Images is provided which subsequently follows glimpses of available well-known datasets. Since all types of cameras experience price reduction over the years, in near future integration and usage of such cameras would be common also because of its illumination invariant characteristic. It becomes evident at the end of the paper that there is a definite scope of developing promising and robust FER with use of Infrared and Thermal images.