Detection of Deception Using Facial Expressions Based on Different Classification Algorithms

Thannoon, Harith H.; Ali, Wissam H.; Hashim, Ivan A.

doi:10.1109/scee.2018.8684170

Cited by 16 publications

(9 citation statements)

References 18 publications

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“…The performances of MLP models were measured by accuracy in 11 [39,40,43,56,66,67,71,72,83,89,102] out of the 12 studies. Those accuracy rates range from 0.6333 to 0.9665, with a mean at 0.7961 ± 0.1130.…”

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: Artificial Neuralmentioning

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%

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

et al. 2023

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“…The second extracted feature for eye gaze in the x direction is that extracted by applying Sign function. The (7) shows the expression of sign function. The output from the sign function is applied to a sum operation.…”

Section: Eye Gaze Angle In X Directionmentioning

confidence: 99%

“…This study was done on 10 participants with detection accuracy of 58.25%. Thannoon et al [7] designed a DDS based on using facial expressions, these expressions are encoded based on facial action coding system (FACS). This system This paper is aimed to design a DDS based on hybrid (combined) features extraction technique.…”

Section: Introductionmentioning

confidence: 99%

Automatic deception detection system based on hybrid feature extraction techniques

Hameed

Hashim

Jalal

2022

IJEECS

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Human face <span lang="EN-US">is considered as a rich source of non-verbal features. These features have proven their efficiency, so they are used by the deception detection system (DDS) to distinguish liar from innocent subjects. The suggested DDS utilized three kinds of features, these are facial expressions, head movements and eye gaze. Facial expressions are simply encoded and represented in the form of action units (AUs) based on facial action coding system (FACS). Head movements are represented based on both transitions and rotation. For eye gaze features, the eye gaze directional angle in both x-axis and y-axis are extracted. The collected database used to prove validity and robustness of the suggested system contains videos for 102 subjects from both genders with age range 18-55 years. The detection accuracy of the suggested DDS based on applying the logistic regression classifier is equal to 88.0631%. The proposed system has proven its robustness and the achievement of the highest detection accuracy when compared with previously designed systems.</span>

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“…Using facial thermal analysis, Jain et al used a database with data from 16 subjects to study detecting deception [23]. Thannoon, Ali, and Hashim studied the facial expressions in [25]. George, Pai, Pai, and Praharaj studied eye blink count and eye blink duration using an intra -gender database consisting of 15 male subjects and 15 female subjects to study how it can be utilized to detect deception [26].…”

Section: B Non-verbal Featurementioning

confidence: 99%

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

Fernandes

Ullah

2022

IEEE Access

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Over a few decades, a remarkable amount of research has been conducted in the field of speech signal processing particularly on deception detection for security applications. In this study, a comprehensive review on recent machine learning approaches using verbal and non-verbal features is presented for deception detection. A brief overview on different feature extraction techniques, the results of recognition rate, and computational time based on machine learning methods are summarized in a tabular format. In addition, numerous datasets used as primary sources of deception detection in the review articles are also presented in this work. Key findings from the reviewed articles are summarized and a few major issues related to deception detection approaches are examined. A statistical analysis which conducted by extracting the significant information from the eighty-eight (88) scientific papers over the last thirty (30) years are provided in this review paper. The results emphasize on the trends of research in deception detection as well as further research opportunities for researchers as a part of continuous progress.

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Detection of Deception Using Facial Expressions Based on Different Classification Algorithms

Cited by 16 publications

References 18 publications

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

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

Automatic deception detection system based on hybrid feature extraction techniques

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

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