Abstract
Humans use deception daily since it can significantly affect their life and provide a getaway solution for any undesired situation. Deception is either related to low-stakes (e.g. innocuous) or high-stakes (e.g. with harmful situations). Deception investigation importance has increased, and it became a critical issue over the years with the increase of security levels around the globe. Technology has made remarkable achievements in many human life fields, including deception detection. Automated deception detection systems (DDSs) are widely used in different fields, especially for security purposes. The DDS is comprised of multiple stages, each of which should be built/trained to perform intelligently so that the whole system can give the right decision of whether the involved person is telling the truth or not. Thus, different artificial intelligent (AI) algorithms have been utilized by the researchers over the past years. In addition, there are different cues for DDS that have been considered for the previous works, which are either related to verbal or non-verbal cues. This paper presents a review on the basic methods and the used deception detection techniques for the recent 10 years, that were studied and performed in the field of DDS, with a comparison of the deception detection accuracy reached and the number of participants used for system training.
Highlights
Deception is defined as concealing the truth from other individuals using face and body gestures [1].People tend to use deception for many reasons
Non-verbal features These are more likely considered for deception detection systems (DDSs) due to the efficiency and high detection accuracy
Discussion of the Used Deception Detection Techniques A DDS mainly consists of three stages, which are video capturing and pre-processing stage, features extraction stage, and the classification stage
Summary
Deception is defined as concealing the truth from other individuals using face and body gestures [1]. It is inclined to the difficulty of distinguishing the high error rate for false positives for stressed innocent participants, or false negatives when emotions are controlled by guilty participants [13,14,15,16,17]. These problems prompted the use of other methods, yielding more reliable and non-invasive techniques, such as the visual feature extraction from suspects' face and body. The voice tone is considered as a verbal feature from which the researchers can determine the deception state for participants [20]. Figure- 2 shows the results of mean F0 at normal (baseline) and stressed states for 12 participants
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