{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:00:54Z","timestamp":1781107254997,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,8,3]],"date-time":"2019-08-03T00:00:00Z","timestamp":1564790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Alcohol 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/sym11080995","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T03:25:22Z","timestamp":1564975522000},"page":"995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Prediction Model of Alcohol Intoxication from Facial Temperature Dynamics Based on K-Means Clustering Driven by Evolutionary Computing"],"prefix":"10.3390","volume":"11","author":[{"given":"Jan","family":"Kubicek","sequence":"first","affiliation":[{"name":"Department of Cybernetic and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17, listopadu 15, 708 33 Ostrava, Poruba, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominik","family":"Vilimek","sequence":"additional","affiliation":[{"name":"Department of Cybernetic and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17, listopadu 15, 708 33 Ostrava, Poruba, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice","family":"Krestanova","sequence":"additional","affiliation":[{"name":"Department of Cybernetic and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17, listopadu 15, 708 33 Ostrava, Poruba, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9527-4642","authenticated-orcid":false,"given":"Marek","family":"Penhaker","sequence":"additional","affiliation":[{"name":"Department of Cybernetic and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17, listopadu 15, 708 33 Ostrava, Poruba, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Kotalova","sequence":"additional","affiliation":[{"name":"Department of Cybernetic and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17, listopadu 15, 708 33 Ostrava, Poruba, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bastien","family":"Faure-Brac","sequence":"additional","affiliation":[{"name":"P\u00f4le MSTIC; UGA\u2014Polytech Grenoble, IESE, 14 Place du Conseil National de la R\u00e9sistance, 38400 St-Martin-d\u2019H\u00e8res, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cl\u00e9ment","family":"Noel","sequence":"additional","affiliation":[{"name":"P\u00f4le MSTIC; UGA\u2014Polytech Grenoble, IESE, 14 Place du Conseil National de la R\u00e9sistance, 38400 St-Martin-d\u2019H\u00e8res, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radomir","family":"Scurek","sequence":"additional","affiliation":[{"name":"Department of Security Services, V\u0160B\u2014Technical University of Ostrava, Lum\u00edrova 13\/630, 700 30 Ostrava, V\u00fd\u0161kovice, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Augustynek","sequence":"additional","affiliation":[{"name":"Department of Cybernetic and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17, listopadu 15, 708 33 Ostrava, Poruba, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-2587","authenticated-orcid":false,"given":"Martin","family":"Cerny","sequence":"additional","affiliation":[{"name":"Department of Cybernetic and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17, listopadu 15, 708 33 Ostrava, Poruba, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomas","family":"Kantor","sequence":"additional","affiliation":[{"name":"Department of Security Services, V\u0160B\u2014Technical University of Ostrava, Lum\u00edrova 13\/630, 700 30 Ostrava, V\u00fd\u0161kovice, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9454","DOI":"10.1038\/s41598-017-09801-1","article-title":"A preclinical model for identifying rats at risk of alcohol use disorder","volume":"7","author":"Jadhav","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12995-017-0184-3","article-title":"Simultaneous measurement of formic acid, methanol and ethanol in vitreous and blood samples of postmortem by headspace GC-FID","volume":"13","author":"Ghorbani","year":"2018","journal-title":"J. Occup. Med. Toxicol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s00213-017-4772-9","article-title":"Alcohol increases inattentional blindness when cognitive resources are not consumed by ongoing task demands","volume":"235","author":"Harvey","year":"2018","journal-title":"Psychopharmacology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1111\/add.13684","article-title":"Acute alcohol effects on set-shifting and its moderation by baseline individual differences: A latent variable analysis","volume":"112","author":"Korucuoglu","year":"2017","journal-title":"Addiction"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ljungblad, J., H\u00f6k, B., and Ekstr\u00f6m, M. (2016). Development and Evaluation of Algorithms for Breath Alcohol Screening. Sensors, 16.","DOI":"10.3390\/s16040469"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3109\/15563650.2015.1112015","article-title":"Ethanol consumption produces a small increase in circulating MIR-122 in healthy individuals","volume":"54","author":"Mccrae","year":"2016","journal-title":"Clin. Toxicol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1590\/S1984-82502016000100015","article-title":"Prevalence and risk of potentially adverse drug interactions in the treatment of acute alcohol poisoning","volume":"52","author":"Andrade","year":"2016","journal-title":"Braz. J. Pharm. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.forsciint.2015.10.026","article-title":"Comparison of venous blood alcohol concentrations and breath alcohol concentrations measured with Draeger Alcotest 9510 DE Evidential","volume":"258","author":"Hartung","year":"2016","journal-title":"Forensic Sci. Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1016\/j.annepidem.2018.08.006","article-title":"Challenges and common weaknesses in case-control studies on drug use and road traffic injury based on drug testing of biological samples","volume":"28","author":"Gjerde","year":"2018","journal-title":"Ann. Epidemiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1017\/S1049023X1800078X","article-title":"Impact of Patients Presenting with Alcohol and\/or Drug Intoxication on In-Event Health Care Services at Mass-Gathering Events: An Integrative Literature Review","volume":"33","author":"Bullock","year":"2018","journal-title":"Prehosp. Disaster Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1007\/978-3-319-60834-1_28","article-title":"Optimization of the training symbols for minimum mean square error equalizer","volume":"565","author":"Martinek","year":"2018","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1111\/acer.12927","article-title":"Subjective Response to Alcohol as a Research Domain Criterion","volume":"40","author":"Ray","year":"2016","journal-title":"Alcohol. Clin. Exp. Res."},{"key":"ref_13","unstructured":"Zhihua, X., Peng, J., Ying, X., and Ke, L. (2016, January 25). Drunk identification using far infrared imagery based on DCT features in DWT domain. Proceedings of the International Symposium on Optoelectronic Technology and Application, Beijing, China."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.forsciint.2015.04.022","article-title":"Neural networks for identifying drunk persons using thermal infrared imagery","volume":"252","author":"Koukiou","year":"2015","journal-title":"Forensic Sci. Int."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hiass, R.S., Arandjelovi\u0107, O., Bendada, H., and Maldague, X. (2013, January 14\u201318). Vesselness features and the inverse compositional AAM for robust face recognition sing thermal IR. Proceedings of the 27th AAAI Conference on Artificial Intelligence, Bellevue, WA, USA.","DOI":"10.1609\/aaai.v27i1.8628"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bhuyan, M.K., Dhawle, S., Sasmal, P., and Koukiou, G. (2018, January 22\u201324). Intoxicated Person Identification Using Thermal Infrared Images and Gait. Proceedings of the 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.","DOI":"10.1109\/WiSPNET.2018.8538761"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1504\/IJESDF.2012.049747","article-title":"Drunk person identification using thermal infrared images","volume":"4","author":"Koukiou","year":"2012","journal-title":"Int. J. Electron. Secur. Digit. Forensics"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Koukiou, G., and Anastassopoulos, V. (2011, January 3\u20134). Facial blood vessels activity in drunk persons using thermal infrared. Proceedings of the 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011), London, UK.","DOI":"10.1049\/ic.2011.0108"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1111\/1556-4029.12989","article-title":"Drunk person screening using eye thermal signatures","volume":"61","author":"Koukiou","year":"2016","journal-title":"J. Forensic Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.infrared.2015.02.007","article-title":"Classification of factors influencing the use of infrared thermography in humans: A review","volume":"71","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.infrared.2009.05.002","article-title":"Infrared thermography on ocular surface temperature: A review","volume":"52","author":"Tan","year":"2009","journal-title":"Infrared Phys. Technol."},{"key":"ref_22","first-page":"99","article-title":"Influence of the field of view on temperature readings from thermal images","volume":"15","author":"Ammer","year":"2005","journal-title":"Thermol. Int."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Koukiou, G., and Anastassopoulos, V. (2016, January 4\u20136). Drunk person identification using local difference patterns. Proceedings of the 2016 IEEE International Conference on Imaging Systems and Techniques (IST), Chania, Greece.","DOI":"10.1109\/IST.2016.7738259"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Socolinsky, D.A., and Selinger, A. (2004, January 26). Thermal face recognition over time Proceedings. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1333735"},{"key":"ref_25","unstructured":"Socolinsky, D.A., and Selinger, A. (2002, January 11\u201315). A comparative analysis of face recognition performance with visible and thermal infrared imagery. Proceedings of the Object Recognition Supported by User Interaction for Service Robots, Quebec City, QC, USA."},{"key":"ref_26","first-page":"540","article-title":"Physiology-based face recognition in the thermal infrared spectrum","volume":"10","author":"Shirizadeh","year":"2013","journal-title":"Life Sci. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1145\/1152934.1152939","article-title":"Automated Facial Expression Classification and affect interpretation using infrared measurement of facial skin temperature variations","volume":"1","author":"Khan","year":"2006","journal-title":"ACM Trans. Auton. Adapt. Syst."},{"key":"ref_28","first-page":"437","article-title":"An Automatic Face Recognition System in the Near Infrared Spectrum","volume":"3587","author":"Zhao","year":"2005","journal-title":"Comput. Vis. ECCV"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hermosilla, G., Verdugo, J.L., Farias, G., Vera, E., Pizarro, F., and Machuca, M. (2018). Face Recognition and Drunk Classification Using Infrared Face Images. J. Sens., 2018.","DOI":"10.1155\/2018\/5813514"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1177\/1403494809353506","article-title":"Psychosocial characteristics of drunk drivers assessed by the Addiction Severity Index, prediction of relapse","volume":"38","author":"Hubicka","year":"2010","journal-title":"Scand. J. Public Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1462055.1462061","article-title":"Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature","volume":"6","author":"Khan","year":"2009","journal-title":"ACM Trans. Appl. Percept."},{"key":"ref_32","first-page":"770","article-title":"A state evaluation adaptive differential evolution algorithm for fir filter design","volume":"15","author":"Wang","year":"2017","journal-title":"Adv. Electr. Electron. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, I., Bronte, S., Bergasa, L.M., Hernandez, N., Delgado, B., and Sevillano, M. (2010, January 19\u201322). Vision-based drowsiness detector for a realistic driving simulator. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal.","DOI":"10.1109\/ITSC.2010.5625097"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Voisan, E.-I., Marginean, O., Precup, R.-E., Dragan, F., and Purcaru, C. (2013, January 23\u201325). Performance evaluation of a face detection algorithm running on general purpose operating systems. Proceedings of the 2013 IEEE 8th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania.","DOI":"10.1109\/SACI.2013.6608959"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/978-3-030-01054-6_17","article-title":"Face detection and recognition for automatic attendance system","volume":"868","author":"Sanli","year":"2019","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.swevo.2018.04.011","article-title":"Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey","volume":"44","author":"Ma","year":"2019","journal-title":"Swarm Evolut. Comput."},{"key":"ref_37","first-page":"113","article-title":"Nature inspired optimization techniques for image processing\u2014A short review","volume":"150","author":"Vishnu","year":"2019","journal-title":"Intell. Syst. Ref. Libr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1109\/TITB.2009.2017017","article-title":"Rough sets and near sets in medical imaging: A review","volume":"13","author":"Hassanien","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_39","unstructured":"Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.asoc.2014.10.020","article-title":"A directed artificial bee colony algorithm","volume":"26","author":"Kiran","year":"2015","journal-title":"Appl. Soft Comput. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1016\/j.eswa.2014.09.049","article-title":"Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur\u2019s, Otsu and Tsallis functions","volume":"42","author":"Bhandari","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6303","DOI":"10.1109\/ACCESS.2017.2780985","article-title":"Detail-Preserving Image Denoising via Adaptive Clustering and Progressive PCA Thresholding","volume":"6","author":"Zhao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1002\/mrm.27084","article-title":"Quality evaluation of no-reference MR images using multidirectional filters and image statistics","volume":"80","author":"Jang","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.compag.2018.06.010","article-title":"Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras","volume":"151","author":"Zhang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.dsp.2018.04.002","article-title":"Multimodal image fusion using sparse representation classification in tetrolet domain","volume":"79","author":"Shahdoosti","year":"2018","journal-title":"Digit. Sign. Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13197","DOI":"10.1007\/s11042-017-4941-1","article-title":"A novel hybrid of DCT and SVD in DWT domain for robust and invisible blind image watermarking with optimal embedding strength","volume":"77","author":"Kang","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.neucom.2018.02.050","article-title":"Referenceless quality metric of multiply-distorted images based on structural degradation","volume":"290","author":"Dai","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.optlaseng.2018.06.011","article-title":"A blind-deblurring method based on a compressed-sensing scheme in digital breast tomosynthesis","volume":"110","author":"Kim","year":"2018","journal-title":"Opt. Lasers Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.engappai.2018.03.001","article-title":"An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm","volume":"71","author":"Mittal","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1109\/TIP.2017.2781307","article-title":"No Reference Quality Assessment for Screen Content Images with Both Local and Global Feature Representation","volume":"27","author":"Fang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/978-981-10-8863-6_14","article-title":"Early started hybrid denoising technique for medical images","volume":"727","author":"Das","year":"2019","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/978-3-030-01821-4_4","article-title":"Perspectives of fast clustering techniques","volume":"875","author":"Savvas","year":"2019","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_53","first-page":"144","article-title":"Temporal data warehouse logical modelling","volume":"8","author":"Garani","year":"2016","journal-title":"Int. J. Data Min. Model. Manag."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/8\/995\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:13:02Z","timestamp":1760188382000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/8\/995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,3]]},"references-count":53,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["sym11080995"],"URL":"https:\/\/doi.org\/10.3390\/sym11080995","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,3]]}}}