Spontaneous Emotion Recognition from Facial Thermal Images

TL;DR

This paper explores the use of modern learning-based methods for spontaneous emotion recognition from facial thermal images, addressing multiple facial analysis tasks with a focus on thermal infrared data.

Contribution

It demonstrates the application of machine learning algorithms to facial landmark localization in thermal images, a task often handled by rule-based methods or left unsolved.

Findings

Successful training of machine learning models on thermal facial data

Potential for improved emotion recognition in thermal imaging

Advancement in thermal face analysis techniques

Abstract

One of the key research areas in computer vision addressed by a vast number of publications is the processing and understanding of images containing human faces. The most often addressed tasks include face detection, facial landmark localization, face recognition and facial expression analysis. Other, more specialized tasks such as affective computing, the extraction of vital signs from videos or analysis of social interaction usually require one or several of the aforementioned tasks that have to be performed. In our work, we analyze that a large number of tasks for facial image processing in thermal infrared images that are currently solved using specialized rule-based methods or not solved at all can be addressed with modern learning-based approaches. We have used USTC-NVIE database for training of a number of machine learning algorithms for facial landmark localization.