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

Fernandes, Sinead V.; Ullah, Muhammad Sana

doi:10.1109/access.2022.3157821

Cited by 7 publications

(2 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers [48] have offered a comprehensive review of contemporary machine learning methodologies in the realm of deception detection, encompassing both verbal and non-verbal features. This paper provides a comprehensive overview of diverse feature extraction techniques, recognition rates, and computational timeframes within the context of machine learning methods.…”

Section: Resultsmentioning

confidence: 99%

Neural Network Applications in Polygraph Scoring—A Scoping Review

Rad,

Paraschiv,

Kiss

2023

Information

View full text Add to dashboard Cite

Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.

show abstract

Section: Resultsmentioning

confidence: 99%

Neural Network Applications in Polygraph Scoring—A Scoping Review

Rad,

Paraschiv,

Kiss

2023

Information

View full text Add to dashboard Cite

show abstract

“…Numerous research in the literature has employed acoustic features to identify deception [72]. The acoustic features provide a non-intrusive experience to detect deception.…”

Section: E Deception Detection Using Acoustic Modalitymentioning

confidence: 99%

A Comprehensive Study of Various Modalities Used for Deception Detection

Harishbhai,

Vaghela

2023

IJCDS

View full text Add to dashboard Cite

Deception detection is an investigative method to determine if someone is telling the truth or fabricating information. It has attracted a lot of study interest because of its potential to be helpful in a variety of real-life problems, including healthcare, law enforcement, internet fraud, criminal investigation, and national security systems. Conventional methods such as the polygraph, demeanor observation, electroencephalogram, and functional magnetic resonance imaging (fMRI) are available to detect deception. These methods are unreliable because they require human interaction and training. They are also time-consuming and costly. Therefore, researchers developed machine learning-driven algorithms to remove human dependency. They have explored thermal imaging, acoustic analysis, eye tracking, facial micro-expression processing, and linguistic analysis to detect deception using machine learning. These techniques may produce better results because they are human-independent and unaffected by race or ethnicity. One can achieve a more reliable automatic deception detection system using features from multiple modalities. This study investigates the feasibility of using linguistic, speech, thermal, and video modalities for automatic deception detection. This paper intends to present a detailed analysis of various deception detection data sets, modalities, and possible directions for the field's development in the future.

show abstract