Emotion Transformation Feature: Novel Feature For Deception Detection In Videos

Yang, Jun-Teng; Liu, Guei-Ming; Huang, Scott C.-H.

doi:10.1109/icip40778.2020.9190846

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…The selected papers explore several different modalities and features, but only two are directly related to emotions. One is a paper from 2020 that delves specifically into emotion transformation [60] combined with visual features (a bimodal approach, see section 3.4 in S6 PLOS ONE File). An SVM classifier trained from visual cues (eye gazing, facial expressions, and hand motions) reached 0.8759 accuracy.…”

Section: Emotional Featuresmentioning

confidence: 99%

“…Statistical analysis reveals that all those textual Monomodal approaches trained their classifiers with data extracted from non-real-life situations and online deception game sessions. Only a few visual and vocal Monomodal studies built their classifiers from real-life data [60,73,77].…”

Section: Complexity and Performancementioning

confidence: 99%

“…Among the studies that use Linear SVM, 18 [39,43,44,54,56,60,62,66,67,72,74,76,85,88,91,98,99,116] measured their performance by accuracy, ranging from 0.5516 to 1.0, with mean at 0.7752 ± 0.1121. Three studies [68,79,95] measured their performance by F1-score, which a range from 0.6012 to 0.7800 and mean at 0.7061 ± 0.0709.…”

Section: Artificial Neuralmentioning

confidence: 99%

“…Among the 20 studies that exploited Random Forest, 16 [40,48,56,60,66,73,75,77,78,84,86,89,92,98,105,114] measured their performance by accuracy, which ranges from 0.5677 to 0.9980, with mean at 0.7301 ± 0.1182. Three other studies [52,57,87] measured their performance by Area Under the Curve, which values 0.7330, 0.8131, and 0.8970.…”

Section: Artificial Neuralmentioning

confidence: 99%

“…KNN appeared 10 times (8.85%), among which eight [43,60,61,67,72,73,80,98] in its vanilla flavor. Those measured performance by accuracy, which ranges from 0.5723 to 0.8688, with a mean at 0.7527 ± 0.1029.…”

Section: Artificial Neuralmentioning

confidence: 99%

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Deception detection with machine learning: A systematic review and statistical analysis

et al. 2023

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Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and results is now available. Therefore, one may find it difficult to identify trends, successful paths, gaps, and opportunities for contribution. The present literature review aims to provide the state of research regarding deception detection with Machine Learning. We followed the PRISMA protocol and retrieved 648 articles from ACM Digital Library, IEEE Xplore, Scopus, and Web of Science. 540 of them were screened (108 were duplicates). A final corpus of 81 documents has been summarized as mind maps. Metadata was extracted and has been encoded as Python dictionaries to support a statistical analysis scripted in Python programming language, and available as a collection of Jupyter Lab Notebooks in a GitHub repository. All are available as Jupyter Lab Notebooks. Neural Networks, Support Vector Machines, Random Forest, Decision Tree and K-nearest Neighbor are the five most explored techniques. The studies report a detection performance ranging from 51% to 100%, with 19 works reaching accuracy rate above 0.9. Monomodal, Bimodal, and Multimodal approaches were exploited and achieved various accuracy levels for detection. Bimodal and Multimodal approaches have become a trend over Monomodal ones, although there are high-performance examples of the latter. Studies that exploit language and linguistic features, 75% are dedicated to English. The findings include observations of the following: language and culture, emotional features, psychological traits, cognitive load, facial cues, complexity, performance, and Machine Learning topics. We also present a dataset benchmark. Main conclusions are that labeled datasets from real-life data are scarce. Also, there is still room for new approaches for deception detection with Machine Learning, especially if focused on languages and cultures other than English-based. Further research would greatly contribute by providing new labeled and multimodal datasets for deception detection, both for English and other languages.

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Section: Emotional Featuresmentioning

confidence: 99%

Section: Complexity and Performancementioning

confidence: 99%