Detection and semantic segmentation of human faces in low resolution thermal images

Type: MA thesis

Status: finished

Date: October 1, 2020 - April 1, 2021

Supervisors: Andreas Maier, Michael Lechner

The detection and isolation of persons with elevated body core temperatures contribute to the reduction of the speed with which certain respiratory diseases are spreading throughout the population. Contactless temperature measurements with thermal cameras are used for fast screening of persons and for selecting those that should be checked more closely with accurate medical thermometers. The only accessible source for temperature information is typically only the face in public areas and its exposed skin segments. The offset between the person’s actual body core temperature and the skin temperature varies in a wide range. It depends on the ambient conditions, what the person did in the last few minutes and where he/ she came from, on the person’s body characteristics and of course it depends on the location of the observed skin segment, not to speak of any technical limitations from the camera itself. Currently the Bosch Sicherheitssysteme Engineering GmbH is investigating the dependency of the body core temperature offset on the location of the measured skin segment.
In this master thesis, a reasonable detection and semantic segmentation of the human face on a thermal image should be investigated. In order to do this, the following points shall be addressed:
– Literature research for state-of-the-art methods of face detection within thermal images
– Identification of the most effective method for the exact face position detection within a preselected image area, including prototypical implementation (e.g. with OpenCV)
– Preparation and annotation of thermal image data for the usage of face detection
– Comparison of a neural network based method for face detection and classical machine learning approaches for the application to low resolution thermal images, eventually including prototypical implementation
– Identification of the most promising methods to correlate a hotspot pixel location with a face section (chin, cheek, nose, forehead, etc.), including prototypical implementations
– Optional: Identification of the most promising methods to detect certain facial occlusions like facial hair (forehead, beard) or glasses, including prototypical implementation
As input for the investigation, existing field test data is available for analysis, but further dedicated lab experiments will certainly be required.