International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022
p-ISSN: 2395-0072
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A Review of Lie Detection Techniques Sreeja Kumari S1, Arya J Nair 2, Sandhya S3 1,2,3Lecturer,
Dept. of Computer Engineering, NSS Polytechnic College, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract –A claim that is thought to be untrue and is often 2. LITERATURE REVIEW made with the intent to deceive someone is called a lie. The lie is challenging to distinguish from the truth as the variations between true and false claims are so negligible. Lying requires more cognitive effort than stating the truth because the liar must work hard to close all the gaps in the lie. When feeling fear, anxiety, or extreme excitement, a person's oxygen consumption rate, BP, galvanic skin resistance, etc. will significantly increase. This is the basis of lie detection. Lies can be detected psychologically by probing for details, asking unexpected questions, and exerting cognitive strain on the subject. Recently, deception detection has advanced beyond polygraphs to include electroencephalography, eye blink patterns, voice signals, etc. This paper presents the various advanced lie detection techniques and their comparison.
An extreme learning machine (ELM) and SLFN (Single layer feed-forward network) based machine learning have been applied to lie detection tasks in [4]. The system makes use of the Vertical and horizontal Electrooculogram (EOG), and Electroencephalograph (EEG) signals for lie detection. This method achieves maximum classification accuracy of 97% with a very short training and testing period. Based on deep learning, a multimodal fusion network detects lies by combining text, audio, and visual information [5]. Visual attributes are collected from the videos by employing a 3D CNN. This system provides higher accuracy of 92% and 96% respectively for late and early fusion approaches. A Lie Detection System based on Machine Learning is built using a wearable EEG headset in [6]. Feature extraction of signals is done using a 3-level Discrete wavelet transform and classification of features is done using a Support Vector Machine. This lie detection approach gives an accuracy of 83%.
Key Words: DIFCW, Machine learning, Extreme learning machine (ELM) and SLFN (Single layer feed-forward network), Electrooculogram (EOG), Electroencephalograph (EEG), Convolutional Neural Network (CNN), Discrete Wavelet Transform (DWT), Bidirectional Long Short Term Memory (Bi-LSTM),) Principal Component Analysis (PCA)
Another EEG signal-based lie detection method using CNN has been proposed in [3]. The suggested model is trained and evaluated using data from the Dryad dataset and a newly created EEG lie dataset. The CNN employed in this system consists of 4 Convolutional layers. The suggested model performed well on the Dryad dataset with an accuracy of 84.44%, whereas it performed poorly on the EEG lie detection dataset with an accuracy of 82.00%. A deep learning approach has been developed to detect lies by leveraging microfeatures and audio features in [7]. This system was based on using a Deep Neural network for lie detection. Audio data was collected by conducting interviews. Real-time facial feature detection is performed by asking a predetermined list of questions. This system achieved an accuracy of 98.45%.
1. INTRODUCTION A lie implies conduct in which a person makes a conscious decision to deceive another person without revealing the intent behind the lie [1][2]. Lie detection aims to uncover hidden facts known to one individual but kept from others. It is difficult to know if somebody is lying. The task of accurate lie detection is intriguing for scientists working in several disciplines. It is essential to spot lies in several areas, including law, medicine, and criminal justice. Despite its reliance on polygraphs as an alternative method for detecting lies, this method has serious reliability issues. Observation and comparison of physiological activity are typically made during a question-and-answer session [3]. Several researchers have sought to identify lies using measures of brain activity, eye blink patterns, and voice signals.
[8] proposed a lie detection system based on DIFCW radars with machine learning. A machine learning algorithm for detecting lies is built based on features extracted from respiratory and heartbeat signals. The system provides 63.2% and 61.5% accuracy for respiratory and heartbeat signals respectively. Another lie detection approach that classifies lies using EEG, auditory, and visual inputs has been proposed in [9]. There are three units in this novel design, one for each data type in the Bag-of-Lies dataset. EEG classification unit consists mainly of a Bi-directional LSTM network. This novel lie detection system has 83.5% accuracy in the lie detection problem.
This article examines various techniques for lie detection besides polygraph and the different methods presented in chapter 3. Their comparative Analysis is shown in Chapter 4. The conclusion of this article is presented in Chapter 5.
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