Design and Implementation of Deception Detection System Based on Reliable Facial Expression

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

doi:10.36478/jeasci.2019.5002.5011

Cited by 6 publications

(7 citation statements)

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“…The Deception Detection System (DDS) simply consists of three stages organized as shown in Fig. 1 [11] . The first stage is related to video recording and pre-processing and then perform editing process.…”

Section: Methodsmentioning

confidence: 99%

“…This study was performed on 16 participants, and the achieved detection accuracy was about 83.5%. Thannoon et al [11] designed DDS based on facial expressions, these expressions encoded based on FACS in a form of AUs. They collected a database for 43 participants and the detected AUs applied to Virtual Generalizing Random Access Memory Weightless Neural Network (VG-RAM WNN) classifier.…”

Section: Iraqi Journal Of Information and Communications Technology(i...mentioning

confidence: 99%

“…FACS simply use Action Unit (AU) as a description of the facial muscles movement. Different studies presented on using AUs features for discriminating between the liar and truth-teller [11] - [13] . The detection of AUs depends on using two types of features these are: geometry and appearance [15].…”

Section: Features Extractionmentioning

confidence: 99%

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Optimized Action Units Features for Efficient Design of Deception Detection System

Shaimaa¹,

Hashim²,

Jalal³

2021

Iraqi Journal of ICT

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Deception detection is becoming an interesting filed in different areas related to security, criminal investigation, law enforcement and terrorism detection. Recently non-verbal features have become essential features for deception detection process. One of the most important kind of these features is facial expression. The importance of these expressions come from the idea that Human face contain different expressions each of which is directly related to a certain state. In this research paper, facial expressions' data are collected for 102 participants (25 women and 77 men) as video clips. There are 504 clips for lie response and 384 for truth response (total 888 video clips). Facial expressions in a form of Action Units (AUs) are extracted for each frame with video clip. The AUs are encoded based on Facial Action Coding System (FACS) which are 18 AUs. These are: AU 1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 20, 23, 25, 26, 28 and 45. Based on the collected data, only six AUs are the most effective and have a direct impact on the discrimination process between liar and truth teller. These AUs are AU 6, 7, 10, 12, 14 and 28

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Section: Methodsmentioning

confidence: 99%

Section: Iraqi Journal Of Information and Communications Technology(i...mentioning

confidence: 99%

See 1 more Smart Citation

Optimized Action Units Features for Efficient Design of Deception Detection System

Shaimaa¹,

Hashim²,

Jalal³

2021

Iraqi Journal of ICT

Self Cite

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“…Additionally, Rubin (2014) suggested that researchers need to study and understand more deception behaviors in the Asian area. Besides, many researchers have utilized various behavioral cues to build an ADD system, like facial expressions (Thannoon et al, 2019), internal physi-ological measures (Ambach and Gamer, 2018) and even functional brain MRI (Kozel et al, 2009a,b). While these indicators can be useful in detecting deceptions, many of them require expensive and invasive instrumentation that is not practical for real-world applications.…”

Section: Introductionmentioning

confidence: 99%

“Does it Matter When I Think You Are Lying?” Improving Deception Detection by Integrating Interlocutor’s Judgements in Conversations

Chou¹,

Chien²,

Juan³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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It is well known that human is not good at deception detection because of a natural inclination of truth-bias. However, during a conversation, when an interlocutor (interrogator) is being asked explicitly to assess whether his/her interacting partner (deceiver) is lying, this perceptual judgment depends highly on how the interrogator interprets the context of the conversation. While the deceptive behaviors can be difficult to model due to their heterogeneous manifestation, we hypothesize that this contextual information, i.e., whether the interlocutor trusts or distrusts what his/her partner is saying, provides an important condition in which the deceiver's deceptive behaviors are more consistently distinct. In this work, we propose a Judgmental-Enhanced Automatic Deception Detection Network (JEADDN) that explicitly considers interrogator's perceived truths-deceptions with three types of speechlanguage features (acoustic-prosodic, linguistic, and conversational temporal dynamics features) extracted during a conversation. We evaluate our framework on a large Mandarin Chinese Deception Dialog Database. The results show that the method significantly outperforms the current state-of-the-art approach without conditioning on the judgements of interrogators on this database. We further demonstrate that the behaviors of interrogators are important in detecting deception when the interrogators distrust the deceivers. Finally, with the late fusion of audio, text, and turntaking dynamics (TTD) features, we obtain promising results of 87.27% and 94.18% accuracy under the conditions that the interrogators trust and distrust the deceivers in deception detection which improves 7.27% and 13.57% than the model without considering the judgements of interlocutor respectively.

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“…acoustic features) to train the detection framework for settings of employment interviews. Thannoon et al [16] used facial expression features to characterize microvariations of the deceiver's face during interview conversation. Other literature also utilized behaviors of language use (e.g.…”

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confidence: 99%

Automatic Deception Detection using Multiple Speech and Language Communicative Descriptors in Dialogs

Chou

Liu

Lee

2021

SIP

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While deceptive behaviors are a natural part of human life, it is well known that human is generally bad at detecting deception. In this study, we present an automatic deception detection framework by comprehensively integrating prior domain knowledge in deceptive behavior understanding. Specifically, we compute acoustics, textual information, implicatures with non-verbal behaviors, and conversational temporal dynamics for improving automatic deception detection in dialogs. The proposed model reaches start-of-the-art performance on the Daily Deceptive Dialogues corpus of Mandarin (DDDM) database, 80.61% unweighted accuracy recall in deception recognition. In the further analyses, we reveal that (i) the deceivers’ deception behaviors can be observed from the interrogators’ behaviors in the conversational temporal dynamics features and (ii) some of the acoustic features (e.g. loudness and MFCC) and textual features are significant and effective indicators to detect deception behaviors.

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Design and Implementation of Deception Detection System Based on Reliable Facial Expression

Cited by 6 publications

References 0 publications

Optimized Action Units Features for Efficient Design of Deception Detection System

Optimized Action Units Features for Efficient Design of Deception Detection System

“Does it Matter When I Think You Are Lying?” Improving Deception Detection by Integrating Interlocutor’s Judgements in Conversations

Automatic Deception Detection using Multiple Speech and Language Communicative Descriptors in Dialogs

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