Maria De Marsico, Giordano Dionisi, Donato Francesco Pio Stanco
{"title":"FTM: The Face Truth Machine—Hand-crafted features from micro-expressions to support lie detection","authors":"Maria De Marsico, Giordano Dionisi, Donato Francesco Pio Stanco","doi":"10.1016/j.cviu.2024.104188","DOIUrl":null,"url":null,"abstract":"<div><div>This work deals with the delicate task of lie detection from facial dynamics. The proposed Face Truth Machine (FTM) is an intelligent system able to support a human operator without any special equipment. It can be embedded in the present infrastructures for forensic investigation or whenever it is required to assess the trustworthiness of responses during an interview. Due to its flexibility and its non-invasiveness, it can overcome some limitations of present solutions. Of course, privacy issues may arise from the use of such systems, as often underlined nowadays. However, it is up to the utilizer to take these into account and make fair use of tools of this kind. The paper will discuss particular aspects of the dynamic analysis of face landmarks to detect lies. In particular, it will delve into the behavior of the features used for detection and how these influence the system’s final decision. The novel detection system underlying the Face Truth Machine is able to analyze the subject’s expressions in a wide range of poses. The results of the experiments presented testify to the potential of the proposed approach and also highlight the very good results obtained in cross-dataset testing, which usually represents a challenge for other approaches.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224002698","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This work deals with the delicate task of lie detection from facial dynamics. The proposed Face Truth Machine (FTM) is an intelligent system able to support a human operator without any special equipment. It can be embedded in the present infrastructures for forensic investigation or whenever it is required to assess the trustworthiness of responses during an interview. Due to its flexibility and its non-invasiveness, it can overcome some limitations of present solutions. Of course, privacy issues may arise from the use of such systems, as often underlined nowadays. However, it is up to the utilizer to take these into account and make fair use of tools of this kind. The paper will discuss particular aspects of the dynamic analysis of face landmarks to detect lies. In particular, it will delve into the behavior of the features used for detection and how these influence the system’s final decision. The novel detection system underlying the Face Truth Machine is able to analyze the subject’s expressions in a wide range of poses. The results of the experiments presented testify to the potential of the proposed approach and also highlight the very good results obtained in cross-dataset testing, which usually represents a challenge for other approaches.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems