Lie detection in interrogations using digital signal processing of brain waves

Amir, Samreen; Ahmed, Naveed; Chowdhry, Bhawani Shankar

doi:10.1109/icici-bme.2013.6698494

Cited by 10 publications

(12 citation statements)

References 10 publications

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“…Finally, Support Vector Machine SVM method used in classification by measuring the distance between the nearest pattern from each class and the corresponding hyperplane. Figure (5) show the different in P300 signals between a liar and non-liar subjects.…”

Section: Deception Detection System Based On Brain Functionmentioning

confidence: 99%

“…The main challenges of deception detector, in general, are the number of participants and accuracy. From the Table 1 that contains the summary of some previous work, where the high accuracy was in [4] when they used facial microexpressions, but the number of participants was small when compared with others [5]. The highest number of participant is shown in table 1 with reasonable accuracy and both of them used facial expression (Eye blink is part of facial expression).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Deceptive Detection Systems and Technologies

Thannoon¹,

Ali²,

Hashim³

2019

ETJ

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Section: Deception Detection System Based On Brain Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Deceptive Detection Systems and Technologies

Thannoon¹,

Ali²,

Hashim³

2019

ETJ

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“…Machine learning algorithms are useful to predict and classify the data by supervised and unsupervised algorithms such as classification and clustering methods. EEG signal analysis is also very useful in the forensic laboratory for detecting the lie; when any individual lies, their signals differ as compared to normal signal which helps to predict and thus it is also used as lie detector described by Amir et al, [2].…”

Section: Literature Surveymentioning

confidence: 99%

“…Then, we can tell whether the suspect knows about the crime or not by analyzing the suspect's brain waves signals. If he/she knows or if he/she present at the moment of crime, his/her brain signals differ from normal signals [2]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Scene and Object Classification Using Brain Waves Signal

Khade

Ilakiyaselvan

2017

Asian J Pharm Clin Res

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.in/ilakiyaselvan.n@vit.ac.inThis study aims to classify the scene and object using brain waves signal. The dataset captured by the electroencephalograph (EEG) device by placing the electrodes on scalp to measure brain signals are used. Using captured EEG dataset, classifying the scene and object by decoding the changes in the EEG signals. In this study, independent component analysis, event-related potentials, and grand mean are used to analyze the signal. Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data. This technique is useful in forensic as well as in artificial intelligence for developing future technology.

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“…Different studies are performed in this filed and each of them used either verbal or non-verbal cues to detect deception. A study performed by Amir et al [4] by designing DDS based on measuring brain wave. These waves are detected and measured for 18 subjects.…”

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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Lie detection in interrogations using digital signal processing of brain waves

Cited by 10 publications

References 10 publications

A Survey on Deceptive Detection Systems and Technologies

A Survey on Deceptive Detection Systems and Technologies

Scene and Object Classification Using Brain Waves Signal

Automatic deception detection system based on hybrid feature extraction techniques

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