A Review of Automatic Lie Detection from Facial Features.
The growth of machine learning and artificial intelligence has made it possible for automatic lie detection systems to emerge. These can be based on a variety of cues, such as facial features. However, there is a lack of knowledge about both the development and the accuracy of such systems. To addre...
| Publicado en: | Journal of Nonverbal Behavior Vol. 48; no. 1; pp. 93 - 137 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Mar2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=176353863&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 176353863 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 01915886 JNV jtl: Journal of Nonverbal Behavior issn: 01915886 maglogo: N pubinfo: dt: Mar2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 176353863 10.1007/s10919-024-00451-2 ppf: 93 ppct: 44 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.1MB tig: atl: A Review of Automatic Lie Detection from Facial Features. aug: au: Delmas, Hugues Denault, Vincent Burgoon, Judee K. Dunbar, Norah E. affil: https://ror.org/0199hds37 Laboratoire UTRPP, Université Sorbonne Paris Nord, Villetaneuse, Paris, France Chercheur associé au laboratoire de l'ENSP, Saint-Cyr-au-Mont-d'Or, France https://ror.org/01pxwe438 McGill University, Montreal, Canada https://ror.org/05f82e368 Laboratoire Cognitions Humaine et Artificielle (EA 4004 - CHArt), Université de Paris 8, Saint-Denis, France https://ror.org/03m2x1q45 University of Arizona, Tucson, USA https://ror.org/02t274463 University of California Santa Barbara, Santa Barbara, USA su: Health Deception Lie detectors & detection Facial expression Bibliographic databases Prompts (Psychology) Prediction models Research evaluation Descriptive statistics Systematic reviews Support vector machines Conceptual structures Machine learning sug: subj: Health Deception Lie detectors & detection Facial expression Investigation Services Bibliographic databases Prompts (Psychology) Prediction models Research evaluation Descriptive statistics Systematic reviews Support vector machines Conceptual structures Machine learning keyword: Deception detection Facial features Review Deception detection Facial features Review ab: The growth of machine learning and artificial intelligence has made it possible for automatic lie detection systems to emerge. These can be based on a variety of cues, such as facial features. However, there is a lack of knowledge about both the development and the accuracy of such systems. To address this lack, we conducted a review of studies that have investigated automatic lie detection systems by using facial features. Our analysis of twenty-eight eligible studies focused on four main categories: dataset features, facial features used, classifier features and publication features. Overall, the findings showed that automatic lie detection systems rely on diverse technologies, facial features, and measurements. They are mainly based on factual lies, regardless of the stakes involved. On average, these automatic systems were based on a dataset of 52 individuals and achieved an average accuracy ranging from 61.87% to 72.93% in distinguishing between truth-tellers and liars, depending on the types of classifiers used. However, although the leakage hypothesis was the most used explanatory framework, many studies did not provide sufficient theoretical justification for the choice of facial features and their measurements. Bridging the gap between psychology and the computational-engineering field should help to combine theoretical frameworks with technical advancements in this area. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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