Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches

Abstract

In recent years, thermal infrared imaging has gained an increasing attention in person monitoring tasks due to its numerous advantages such as illumination invariance and its ability to monitor vital parameters directly. Many of these applications require facial region monitoring. In this context, several methods for face detection in thermal infrared images have been developed. Nearly all of the approaches introduced in this context make use of specific properties of facial images in the thermal infrared domain, such as local temperature maxima in the eye area or the fact that human bodies usually have a higher temperature radiation than the backgrounds used. On the other side, a number of well-performing methods for face detection in the visual spectrum has been introduced in recent years. These approaches use state-of-the-art algorithms from machine learning and feature extraction to detect faces in photographs and videos. So far, only one of these algorithms has been successfully applied to thermal infrared images. In our work, we therefore analyze how a larger number of these algorithms can be adapted to thermal infrared images and show that a wide number of recently introduced algorithms for face detection in the visual spectrum can be trained to work in the thermal spectrum when an appropriate training database is available. Our evaluation shows that these machine-learning based approaches outperform thermal-specific solutions in terms of detection accuracy and false positive rate. In conclusion, we can show that well-performing methods introduced for face detection in the visual spectrum can also be used for face detection in thermal infrared images, making dedicated thermal-specific solutions unnecessary.