Thermal Biometric Features for Drunk Person Identification Using Multi-Frame Imagery

Abstract

In this work, multi-frame thermal imagery of the face of a person is employed for drunk identification. Regions with almost constant temperature on the face of sober and drunk persons are thoroughly examined for their capability to discriminate intoxication. Novel image processing approaches as well as feature extraction techniques are developed to support the drunk identification procedure. These techniques constitute novel ideas in the theory of image analysis and algorithm development. Nonlinear anisotropic diffusion is employed for a light smoothing on the images before feature extraction. Feature vector extraction is based on morphological operations performed on the isothermal regions on the face. The classifier chosen to verify the drunk person discrimination capabilities of the procedure is a Support Vector Machine (SVM). Obviously, the isothermal regions on the face change their shape and size with alcohol consumption. Consequently, intoxication identification can be carried out based only on the thermal signatures of the drunk person, while the signature of the corresponding sober person is not needed. A sample of 41 participants who drank in a controlled alcohol consumption procedure was employed for creating the database, which contains 4100 thermal images. The proposed method for intoxication identification achieves a success rate of over 86% and constitutes a fast non-invasive test that can replace existing breathalyzer check up.