Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing
AbstractAlcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster’s distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level. View Full-Text
Externally hosted supplementary file 1
Description: Supplementary material contains figures from the manuscript in the .png and .svg format, and analysis of the intoxicated people from IR images.
Share & Cite This Article
Kubicek, J.; Vilimek, D.; Krestanova, A.; Penhaker, M.; Kotalova, E.; Faure-Brac, B.; Noel, C.; Scurek, R.; Augustynek, M.; Cerny, M.; Kantor, T. Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing. Symmetry 2019, 11, 995.
Kubicek J, Vilimek D, Krestanova A, Penhaker M, Kotalova E, Faure-Brac B, Noel C, Scurek R, Augustynek M, Cerny M, Kantor T. Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing. Symmetry. 2019; 11(8):995.Chicago/Turabian Style
Kubicek, Jan; Vilimek, Dominik; Krestanova, Alice; Penhaker, Marek; Kotalova, Eva; Faure-Brac, Bastien; Noel, Clément; Scurek, Radomir; Augustynek, Martin; Cerny, Martin; Kantor, Tomas. 2019. "Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing." Symmetry 11, no. 8: 995.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.