Emotional Intensity Estimation using Thermal Images

Intensity estimation of genuine emotion is a challenge for an inexpressive face or deceiving emotion. Thermal modality is experimentally seen to have the capability to reflect true emotion. However, emotion intensity in thermal images is not studied much due to the lack of an annotated database. In this paper, we propose a thermal domain-specific feature-based approach to estimate the intensity of emotion sequence images. The method uses labeled apex images of all six emotions from the NVIE dataset. For extracting domain-specific features, firstly, linear filters are learned from apex images using convolutional sparse coding. Then we obtain features using those learned filters. Further, a distance-based emotion intensity method is proposed without any knowledge of the actual intensities. For a specific emotion, this is done by finding the distance between features of sequence images and clusters of apex features. Proper clustering is ensured by using a supervised dimensionality reduction method and further verifying using SVM classification. Also, a way is suggested to calculate the error in the absence of the actual intensities. The experimental results on the standard NVIE dataset validate better performance compared to previous methods.