{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T14:57:00Z","timestamp":1791817020221,"version":"4.3.4"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JST SPRING","award":["JPMJSP2123"],"award-info":[{"award-number":["JPMJSP2123"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world\u2019s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection.<\/jats:p>","DOI":"10.3390\/bioengineering10070862","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T05:42:01Z","timestamp":1689831721000},"page":"862","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Detecting Dementia from Face-Related Features with Automated Computational Methods"],"prefix":"10.3390","volume":"10","author":[{"given":"Chuheng","family":"Zheng","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Yokohama 223-0061, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mondher","family":"Bouazizi","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Keio University, Yokohama 223-0061, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-1426","authenticated-orcid":false,"given":"Tomoaki","family":"Ohtsuki","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Keio University, Yokohama 223-0061, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Momoko","family":"Kitazawa","sequence":"additional","affiliation":[{"name":"School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1103-2510","authenticated-orcid":false,"given":"Toshiro","family":"Horigome","sequence":"additional","affiliation":[{"name":"School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taishiro","family":"Kishimoto","sequence":"additional","affiliation":[{"name":"School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023, March 15). Dementia, Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dementia."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"73","DOI":"10.3389\/fneur.2012.00073","article-title":"Behavioral and psychological symptoms of dementia","volume":"3","author":"Cerejeira","year":"2012","journal-title":"Front. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1108\/WWOP-10-2014-0032","article-title":"What do we mean when we talk about dementia? Exploring cultural representations of \u201cdementia\u201d","volume":"19","author":"Zeilig","year":"2015","journal-title":"Work. Older People"},{"key":"ref_4","unstructured":"Downs, M., and Bowers, B.B. (2010). Excellence in Dementia Care: Research into Practice, Open University Press."},{"key":"ref_5","unstructured":"Alzheimer\u2019s Association (2023, May 11). Stages of Alzheimer\u2019s. Available online: https:\/\/www.alz.org\/alzheimers-dementia\/stages."},{"key":"ref_6","unstructured":"UN (2020). Population Division, World Population Ageing 2019 Highlights."},{"key":"ref_7","unstructured":"Alzheimer\u2019s Association (2023, May 11). Alzheimer\u2019s Disease Facts and Figures. Available online: https:\/\/www.alz.org\/alzheimers-dementia\/facts-figures."},{"key":"ref_8","unstructured":"World Health Organization (2015). First WHO Ministerial Conference on Global Action against Dementia: Meeting Report."},{"key":"ref_9","first-page":"520","article-title":"Diagnosis and treatment of dementia","volume":"122","author":"Engedal","year":"2002","journal-title":"Tidsskr. Nor. Laegeforening Tidsskr. Prakt. Med. Raekke"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/j.0959-9673.2005.00435.x","article-title":"Concepts for the treatment of Alzheimer\u2019s disease: Molecular mechanisms and clinical application","volume":"86","author":"Pietrzik","year":"2005","journal-title":"Int. J. Clin. Exp. Pathol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1093\/jamia\/ocaa174","article-title":"A systematic literature review of automatic Alzheimer\u2019s disease detection from speech and language","volume":"27","author":"Petti","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tanaka, H., Adachi, H., Kazui, H., Ikeda, M., Kudo, T., and Nakamura, S. (2019, January 14\u201318). Detecting dementia from face in human-agent interaction. Proceedings of the ICMI 2019, Suzhou, China.","DOI":"10.1145\/3351529.3360654"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.18632\/aging.202545","article-title":"Screening of Alzheimer\u2019s disease by facial complexion using artificial intelligence","volume":"13","author":"Kameyama","year":"2021","journal-title":"Aging"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Weiner, J., Engelbart, M., and Schultz, T. (2017, January 20\u201324). Manual and Automatic Transcriptions in Dementia Detection from Speech. Proceedings of the Interspeech, Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-112"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mirheidari, B., Blackburn, D., Walker, T., Venneri, A., Reuber, M., and Christensen, H. (2018, January 2\u20136). Detecting Signs of Dementia Using Word Vector Representations. Proceedings of the Interspeech, Hyderabad, India.","DOI":"10.21437\/Interspeech.2018-1764"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"624683","DOI":"10.3389\/fcomp.2021.624683","article-title":"Exploring deep transfer learning techniques for Alzheimer\u2019s dementia detection","volume":"3","author":"Zhu","year":"2021","journal-title":"Front. Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"623607","DOI":"10.3389\/fnagi.2021.623607","article-title":"Acoustic and language based deep learning approaches for Alzheimer\u2019s dementia detection from spontaneous speech","volume":"13","author":"Mahajan","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shibata, D., Ito, K., Nagai, H., Okahisa, T., Kinoshita, A., and Aramaki, E. (2018). Idea density in Japanese for the early detection of dementia based on narrative speech. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0208418"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Santander-Cruz, Y., Salazar-Colores, S., Paredes-Garc\u00eda, W.J., Guendulain-Arenas, H., and Tovar-Arriaga, S. (2022). Semantic feature extraction using SBERT for dementia detection. Brain Sci., 12.","DOI":"10.3390\/brainsci12020270"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Luz, S., Haider, F., de la Fuente, S., Fromm, D., and MacWhinney, B. (2020). Alzheimer\u2019s dementia recognition through spontaneous speech: The ADReSS challenge. arXiv.","DOI":"10.21437\/Interspeech.2020-2571"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1001\/archneur.1994.00540180063015","article-title":"The natural history of Alzheimer\u2019s disease: Description of study cohort and accuracy of diagnosis","volume":"51","author":"Becker","year":"1994","journal-title":"Arch. Neurol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Farzana, S., Deshpande, A., and Parde, N. (2022, January 26). How you say it matters: Measuring the impact of verbal disfluency tags on automated dementia detection. Proceedings of the 21st Workshop on Biomedical Language Processing, Dublin, Ireland.","DOI":"10.18653\/v1\/2022.bionlp-1.4"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101485","DOI":"10.1016\/j.csl.2023.101485","article-title":"Detecting dementia from speech and transcripts using transformers","volume":"79","author":"Ilias","year":"2023","journal-title":"Comput. Speech Lang."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1002\/gps.930060809","article-title":"Facial expressions in severely demented patients\u2014A stimulus\u2013response study of four patients with dementia of the Alzheimer type","volume":"6","author":"Asplund","year":"1991","journal-title":"Int. J. Geriatr. Psychiatry"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1017\/S1041610295002262","article-title":"Facial expressions of patients with dementia: A comparison of two methods of interpretation","volume":"7","author":"Asplund","year":"1995","journal-title":"Int. Psychogeriatr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"707809","DOI":"10.3389\/fpsyg.2021.707809","article-title":"The Effectiveness of Facial Expression Recognition in Detecting Emotional Responses to Sound Interventions in Older Adults with Dementia","volume":"12","author":"Liu","year":"2021","journal-title":"Front. Psychol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Seyedi, S., Haque, R.U., Pongos, A.L., Vickers, K.L., Manzanares, C.M., Lah, J.J., Levey, A.I., and Clifford, G.D. (2022). Automated analysis of facial emotions in subjects with cognitive impairment. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262527"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100649","DOI":"10.1016\/j.conctc.2020.100649","article-title":"The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology","volume":"19","author":"Kishimoto","year":"2020","journal-title":"Contemp. Clin. Trials Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.cortex.2014.10.020","article-title":"Fast, but not slow, familiarity is preserved in patients with amnestic mild cognitive impairment","volume":"65","author":"Besson","year":"2015","journal-title":"Cortex"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1093\/cercor\/bhm104","article-title":"Activating the medial temporal lobe during oddity judgment for faces and scenes","volume":"18","author":"Lee","year":"2008","journal-title":"Cereb. Cortex"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1093\/cercor\/bhm140","article-title":"Spatio temporal dynamics of face recognition","volume":"18","author":"Barbeau","year":"2008","journal-title":"Cereb. Cortex"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1016\/j.neuropsychologia.2007.11.007","article-title":"Impaired memory for scenes but not faces in developmental hippocampal amnesia: A case study","volume":"46","author":"Bird","year":"2008","journal-title":"Neuropsychologia"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"654","DOI":"10.3758\/BF03212975","article-title":"Recognition memory for faces: When familiarity supports associative recognition judgments","volume":"6","author":"Yonelinas","year":"1999","journal-title":"Psychon. Bull. Rev."},{"key":"ref_34","first-page":"100","article-title":"Fast and famous: Looking for the fastest speed at which a face can be recognized","volume":"4","author":"Besson","year":"2013","journal-title":"Front. Psychol."},{"key":"ref_35","unstructured":"Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.L., Yong, M.G., and Lee, J. (2019). Mediapipe: A framework for building perception pipelines. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.neucom.2016.09.051","article-title":"Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient","volume":"219","author":"Abbasi","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jianu, S.-R.-S., Ichim, L., Popescu, D., and Chenaru, O. (2018, January 10\u201312). Advanced processing techniques for detection and classification of skin lesions. Proceedings of the 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania.","DOI":"10.1109\/ICSTCC.2018.8540732"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ekman, P., and Friesen, W.V. (1978). Facial Action Coding System: A Technique for the Measurement of Facial Movement, Consulting Psychologists Press.","DOI":"10.1037\/t27734-000"},{"key":"ref_39","unstructured":"Ekman, P., Friesen, W.V., and Ellsworth, P. (2013). Emotion in the Human face: Guidelines for Research and an Integration of Findings, Elsevier."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Zadeh, A., Lim, Y.C., and Morency, L.P. (2018, January 15\u201319). Openface 2.0: Facial behavior analysis toolkit. Proceedings of the FG 2018, Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00019"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Baltru\u0161aitis, T., Mahmoud, M., and Robinson, P. (2015, January 4\u20138). Cross-dataset learning and person-specific normalisation for automatic action unit detection. Proceedings of the 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia.","DOI":"10.1109\/FG.2015.7284869"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Robinson, P., and Morency, L.P. (2013, January 2\u20138). Constrained local neural fields for robust facial landmark detection in the wild. Proceedings of the IEEE International Conference on Computer Vision Workshops, Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.54"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s44163-022-00022-8","article-title":"Quo vadis artificial intelligence?","volume":"2","author":"Jiang","year":"2022","journal-title":"Discov. Artif. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pendrill, L.R., Melin, J., Stavelin, A., and Nordin, G. (2023). Modernising Receiver Operating Characteristic (ROC) Curves. Algorithms, 16.","DOI":"10.3390\/a16050253"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/10\/7\/862\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:15:23Z","timestamp":1760127323000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/10\/7\/862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,20]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["bioengineering10070862"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering10070862","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,20]]}}}