Deception in the eyes of deceiver: A computer vision and machine learning based automated deception detection
- Submitting institution
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Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2386
- Type
- D - Journal article
- DOI
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10.1016/j.eswa.2020.114341
- Title of journal
- Expert Systems with Applications
- Article number
- 114341
- First page
- -
- Volume
- n/a
- Issue
- -
- ISSN
- 0957-4174
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2020
- URL
-
https://e-space.mmu.ac.uk/625531/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
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C - Machine Intelligence
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a methodology to identify micro-movements within the eyes/face which can be combined to distinguish truthful/deceptive behaviours utilizing a machine-learning approach. This work is underpinned by ‘Silent Talker’ (Rothwell et al, 2006) and is funded by H2020-iBorderCtrl (700626). The research confirms that non-verbal cues utilized by automated deception detection do align with findings from human forensic psychologists. The work features in an impact case study and led to place-based policy funding for two Ethical AI roundtables with Policy-Connect (jack.tindale@policyconnect.org.uk and the APPG on Data Analytics which resulted in a working group for a Greater-Manchester Charter for Ethical AI.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -