Lie detection machine learning pdf

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The obtained set of features is given as an input to the extreme learning machine classifier for training of guilty and innocent samples. The performance of the system is assessed using 10-fold cross-validation. The resultant accuracy obtained from the proposed lie detection system is 88.3%. The system has provided efficient results in contrast Using machine learning for lie detection: classification of human visual morphology @inproceedings{Azhan2018UsingML, title={Using machine learning for lie detection: classification of human visual morphology}, author={Samin Azhan and Anik Zaman and Monjur Rakib Bhuiyan}, year={2018} } S. Azhan, A. Zaman, Monjur Rakib Bhuiyan; Published 2018 In this work, a deep learning model is developed to detect lies by analyzing human facial expression. We utilized the emotional leakage in the form of 'fear' to detect lies. An intelligent lie detection model using ResNet-18 deep neural network is developed. The model is evaluated using the (FER-2013) dataset. The model exhibited a good with lie and truth. In 22 participants performing a forced-choice deception task, 99% of the true and false responses were discriminated correctly. Predictive accuracy, assessed by cross-validation in partic-ipants not included in training, was 88%. The results demonstrate the potential of non-linear machine learning techniques in lie detection and Lie Detection based on Facial Micro Expression, Body Language and Speech Analysis. Soumya Barathi C. Computer Science and Engineering . SRM University . Chennai, India. Abstract— Facial micro expressions, body language and analysis of speech are powerful tools of lie detection. Facial micro expression is an involuntary reaction that is impossible to fake. Liars experience stress which