A multilayer perceptron neural network prediction approach to polygraph scoring
DOI:
https://doi.org/10.15837/ijccc.2025.2.7008Keywords:
Polygraph, scoring system, Multilayer Perceptron Neural Network (MLP), deception detectionAbstract
Years of studies have consistently demonstrated that people’s capacity to detect deceit is no better than chance. For law enforcement officers, accurate deception detection is critical. The traditional polygraph examination is now the sole standardized and reliable method for detecting deceit. There are several standardized scoring protocols (Lafayette Polygraph System 11.8.6) to Control Question Technique (CQT) Polygraph examinations: PolyScore, OSS-2, OSS-3 and manually scoring. Due to the ongoing controversy over which scoring system performs better in terms of avoiding false positive and false negative errors, this study introduces a Multilayer Perceptron Neural Network (MLP) prediction approach to Polygraph deception scoring utilizing manually scored examination data. A MLP was trained to predict high and low deception scores in 400 offender data, based on the most predictive psychophysiological indicators found in the scientific literature: amplitude of electrodermal reaction (ARED), amplitude of blood pressure in brachial pulse (ATAB), change of base line level in chest breathing (MNBRT) and difference of altitude between breathing cycles (DIFA). The model predicted the deception level of the 400 offenders with a correct classification rate (CCR) of 80%, result consistent with the prediction accuracy reported in the recent literature. The MLP neural network modeling results showed that based on the four psychophysiological indicators ARED, ATAB, MNBRT and DIFA there is an 80% correct classification rate of high and low deception scores received by insincere subjects. The key outcome of this study suggests that MLP represents a robust approach to identify deception in manually scored polygraph examinations.
References
Albrecht, W. S., Albrecht, C. O., Albrecht, C. C., Zimbelman, M. F. (2018). Fraud examination. Cengage Learning.
Albu, R. D., Dzitac, I., Popentiu-Vlădicescu, F., Naghiu, I. M. (2014). Input Projection Algorithms Influence in Prediction and Optimization of QoS Accuracy. International Journal of Computers Communications & Control, 9(2), 131-138. https://doi.org/10.15837/ijccc.2014.2.1013
Arasteh, A., Moradi, M. H., Janghorbani, A. (2016). A novel method based on empirical mode decomposition for P300-based detection of deception. IEEE Transactions on Information Forensics and Security, 11(11), 2584-2593. https://doi.org/10.1109/TIFS.2016.2590938
Ash, P. (1971). Screening employment applicants for attitudes toward theft. Journal of Applied Psychology, 55(2), 161. https://doi.org/10.1037/h0030788
Baghel, N., Singh, D., Dutta, M. K., Burget, R., Myska, V. (2020, July). Truth identification from EEG signal by using convolution neural network: lie detection. In 2020 43rd International Conference on Telecommunications and Signal Processing (TSP) (pp. 550-553). IEEE. https://doi.org/10.1109/TSP49548.2020.9163497
Baghel, N., Singh, D., Dutta, M. K., Burget, R., & Myska, V. (2020). Truth identification from EEG signal by using convolution neural network: lie detection. In 2020 43rd International Conference on Telecommunications and Signal Processing (TSP) (pp. 550-553). IEEE. https://doi.org/10.1109/TSP49548.2020.9163497
Bashore, T. R., Rapp, P. E. (1993). Are there alternatives to traditional polygraph procedures? Psychological Bulletin, 113(1), 3-22. https://doi.org/10.1037//0033-2909.113.1.3
Ben-Shakhar, G., Bar-Hillel, M., Lieblich, I. (1986). Trial by polygraph: Scientific and juridical issues in lie detection. Behavioral Sciences the Law, 4(4), 459-479. https://doi.org/10.1002/bsl.2370040408
Bhutta, M. R., Hong, M. J., Kim, Y. H., Hong, K. S. (2015). Single-trial lie detection using a combined fNIRS-polygraph system. Frontiers in psychology, 6, 709. https://doi.org/10.3389/fpsyg.2015.00709
Bhuvaneswari, P., Kumar, J. S. (2015). A note on methods used for deception analysis and influence of thinking stimulus in deception detection. International Journal of Engineering and Technology, 7(1), 109-116.
Brouwer, E. (2021). Schengen and the administration of exclusion: legal remedies caught in between entry bans, risk assessment and artificial intelligence. European Journal of Migration and Law, 23(4), 485-507. https://doi.org/10.1163/15718166-12340115
Cook, L. G., Mitschow, L. C. (2019). Beyond the polygraph: Deception detection and the autonomic nervous system. Federal Practitioner, 36(7), 316.
Cook, L. G., Mitschow, L. C. (2019). Beyond the polygraph: Deception detection and the autonomic nervous system. Federal Practitioner, 36(7), 316.
Cross, T. P., Saxe, L. (1993). A critique of the validity of polygraph testing in child sexual abuse cases. Journal of Child Sexual Abuse, 1(4), 19-34. https://doi.org/10.1300/J070v01n04_02
Cunningham, M. R. (1989). Test-taking motivations and outcomes on a standardized measure of on-the-job integrity. Journal of Business and Psychology, 4(1), 119-127. https://doi.org/10.1007/BF01023042
Daneshi Kohan, M., Motie Nasrabadi, A., Shamsollahi, M. B., Sharifi, A. (2020). EEG/PPG effective connectivity fusion for analyzing deception in interview. Signal, Image and Video Processing, 14(5), 907-914. https://doi.org/10.1007/s11760-019-01622-1
Dollins, A. B., Kraphol, D. J., Dutton, D. W. (2000). Computer algorithm comparison. Polygraph, 29(3), 237-257.
Elkins, A. C., Gupte, A., Cameron, L. (2019, July). Humanoid robots as interviewers for automated credibility assessment. In International Conference on Human-Computer Interaction (pp. 316-325). Springer, Cham. https://doi.org/10.1007/978-3-030-22338-0_26
Farrow, T. F., Burgess, J., Wilkinson, I. D., Hunter, M. D. (2015). Neural correlates of selfdeception and impression-management. Neuropsychologia, 67, 159-174. https://doi.org/10.1016/j.neuropsychologia.2014.12.016
Gamer, M. (2014). Mind reading using neuroimaging: Is this the future of deception detection?. European Psychologist, 19(3), 172. https://doi.org/10.1027/1016-9040/a000193
Gamer, M. (2014). Mind reading using neuroimaging: Is this the future of deception detection?. European Psychologist, 19(3), 172. https://doi.org/10.1027/1016-9040/a000193
Giansiracusa, N. (2021). Prevarication and the Polygraph. In How Algorithms Create and Prevent Fake News (pp. 99-118). Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-7155-1_5
Gogate, M., Adeel, A., Hussain, A. (2017, November). Deep learning driven multimodal fusion for automated deception detection. In 2017 IEEE symposium series on computational intelligence (SSCI) (pp. 1-6). IEEE. https://doi.org/10.1109/SSCI.2017.8285382
Gordon, N. J. (2016). Essentials of polygraph and polygraph testing. CRC Press. https://doi.org/10.1201/9781315438641
Gordon, N. J., Mohamed, F. B., Faro, S. H., Platek, S. M., Ahmad, H., Williams, J. M. (2006). Integrated zone comparison polygraph technique accuracy with scoring algorithms. Physiology behavior, 87(2), 251-254. https://doi.org/10.1016/j.physbeh.2005.08.046
Happel, M. D. (2005). Neuroscience and the Detection of Deception. Review of Policy Research, 22(5), 667-685. https://doi.org/10.1111/j.1541-1338.2005.00166.x
Horvath, F. (2020). A Hundred Years of Polygraphy: Some Primary Changes and Related Issues. European Polygraph, 1, 31-45. https://doi.org/10.2478/ep-2020-0007
Kohan, M. D., Nasrabadi, A. M., Shamsollahi, M. B. (2020). Interview based connectivity analysis of EEG in order to detect deception. Medical hypotheses, 136, 109517. https://doi.org/10.1016/j.mehy.2019.109517
Lakshan, I., Wickramasinghe, L., Disala, S., Chandrasegar, S., Haddela, P. S. (2019, October). Real time deception detection for criminal investigation. In 2019 National Information Technology Conference (NITC) (pp. 90-96). IEEE. https://doi.org/10.1109/NITC48475.2019.9114422
Lapadusi, V., Dobreanu, R. (2014). History of Polygraph/Din Istoria Tehnicii Poligraf. Romanian Journal of Forensic Science, 15(95), 1737.
Lo, Y. L., Fook-Chong, S., Tan, E. K. (2003). Increased cortical excitability in human deception. Neuroreport, 14(7), 1021-1024. https://doi.org/10.1097/00001756-200305230-00023
Marouf, A. A., Ashrafi, A. F., Ahmed, T., Emon, T. (2019). A Machine Learning based Approach for Mapping Personality Traits and Perceived Stress Scale of Undergraduate Students. International Journal of Modern Education & Computer Science, 11(8). https://doi.org/10.5815/ijmecs.2019.08.05
Marx, G. T. (1998). Ethics for the new surveillance. The Information Society, 14(3), 171-185. https://doi.org/10.1080/019722498128809
Masip, J. (2017). Deception detection: State of the art and future prospects. Psicothema, 29(2), 149-159. https://doi.org/10.7334/psicothema2017.34
Matte, J. A. (1996). Forensic psychophysiology using the polygraph: Scientific truth verification, lie detection. JAM Publications.
Matte, J. A. (2007). Critical Analysis of Krapohl's Validated Polygraph Techniques. Polygraph, 36(1), 29-34.
Matte, J. A. (2020). Foremost Changes in Polygraph in Last 100 Years. European Polygraph, 14(1 (51)), 47-49. https://doi.org/10.2478/ep-2020-0009
Meijer, E. H., Verschuere, B. (2017). Deception detection based on neuroimaging: Better than the polygraph?. Journal of Forensic Radiology and Imaging, 8, 17-21. https://doi.org/10.1016/j.jofri.2017.03.003
Meijer, E. H., Verschuere, B. (2017). Deception detection based on neuroimaging: Better than the polygraph?. Journal of Forensic Radiology and Imaging, 8, 17-21. https://doi.org/10.1016/j.jofri.2017.03.003
Nagle, D. E. (1983). The Polygraph in the Workplace. U. Rich. L. Rev., 18, 43.
Nelson, R. (2015). Scientific basis for polygraph testing. Polygraph, 44(1), 28-61. https://doi.org/10.1201/9781315438641-2
Nelson, R. (2019). Literature Survey of Structural Weighting of Polygraph Signals: Why Double the EDA?. Polygraph & Forensic Credibility Assessment, 48(2), 105-112.
Nevins, J. L. (2004). Measuring the mind: A comparison of personality testing to polygraph testing in the hiring process. Penn St. L. Rev., 109, 857.
Nortje, A., Tredoux, C. (2019). How good are we at detecting deception? A review of current techniques and theories. South African Journal of Psychology, 49(4), 491-504. https://doi.org/10.1177/0081246318822953
Nuwer, M. R. (1990). On the controversies about clinical use of EEG brain mapping. Brain Topography, 3(1), 103-111. https://doi.org/10.1007/BF01128867
Nuwer, M. R. (1990). The development of EEG brain mapping. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, 7(4), 459-471. https://doi.org/10.1097/00004691-199010000-00002
O'Shea, J., Crockett, K., Khan, W., Kindynis, P., Antoniades, A., Boultadakis, G. (2018). Intelligent deception detection through machine based interviewing. In 2018 International joint conference on neural networks (IJCNN) (pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN.2018.8489392
Oravec, J. A. (2022). The emergence of "truth machines"?: Artificial intelligence approaches to lie detection. Ethics and Information Technology, 24(1), 1-10. https://doi.org/10.1007/s10676-022-09621-6
Oswald, M. (2020). Technologies in the twilight zone: early lie detectors, machine learning and reformist legal realism. International Review of Law, Computers & Technology, 34(2), 214-231. https://doi.org/10.1080/13600869.2020.1733758
Pasca, V. (2012). Study regarding psychophysiological reactivity values depending on subject's gender in polygraph testing. Procedia-Social and Behavioral Sciences, 33, 821-825. https://doi.org/10.1016/j.sbspro.2012.01.236
Pérez-Rosas, V., Abouelenien, M., Mihalcea, R., Burzo, M. (2015, November). Deception detection using real-life trial data. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 59-66). https://doi.org/10.1145/2818346.2820758
Popescu, M. C., Balas, V. E., Perescu-Popescu, L., Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7), 579-588.
Rad, D., Magulod, G.C. Jr., Balas, E., Roman, A., Egerau, A., Maier, R., Ignat, S., Dughi, T., Balas, V., Demeter, E., Rad, G. and Chis, R. (2022). A Radial Basis Function Neural Network Approach to Predict Preschool Teachers' Technology Acceptance Behavior. Front. Psychol. 13:880753. https://doi.org/10.3389/fpsyg.2022.880753
Raskin, D. C. (1987). Methodological issues in estimating polygraph accuracy in field applications. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement, 19(4), 389. https://doi.org/10.1037/h0079999
Raskin, D. C., Kircher, J. C. (2014). Validity of polygraph techniques and decision methods. In Credibility assessment (pp. 63-129). Academic Press. https://doi.org/10.1016/B978-0-12-394433-7.00003-8
Raskin, D. C., Kircher, J. C., Honts, C. R. (2019). Computerized Polygraph Interpretations and Detection of Physical Countermeasures. In Anti-Terrorism; Forensic Science; Psychology in Police Investigations (pp. 179-188). Routledge. https://doi.org/10.4324/9780429036590-20
Raskin, D. C., Kircher, J. C., Honts, C. R., Horowitz, S. W. (2019). A Study of the Validity of Polygraph Examinations in Criminal Investigation: Final Report to the National Institute of Justice Grant No. 85-IJ-CX-0040. Polygraph, 48(1), 10-39.
Saeed, S., Romarheim, A., Mancia, G., Saxvig, I. W., Gulati, S., Lehmann, S., Bjorvatn, B. (2022). Characteristics of hypertension and arterial stiffness in obstructive sleep apnea: A Scandinavian experience from a prospective study of 6408 normotensive and hypertensive patients. The Journal of Clinical Hypertension, 24(4), 385-394. https://doi.org/10.1111/jch.14425
Sánchez-Monedero, J., Dencik, L. (2022). The politics of deception borders:'biomarkers of deceit'and the case of iBorderCtrl. Information, Communication & Society, 25(3), 413-430. https://doi.org/10.1080/1369118X.2020.1792530
Selič, P. (2009). Analytical review of validated polygraph techniques. Journal of Criminal Investigation and Criminology/Ljubljana, 60, 4.
Slowik, S. M. (2013). Evaluating Previously Conducted Polygraph Examinations. Polygraph, 42(4), 203-208.
Slowik, S. M. (2020). Critical Changes Over the 100 Year Evolution of Polygraph Practices. European Polygraph, 14(1 (51)), 50-57. https://doi.org/10.2478/ep-2020-0010
Twyman, N. W., Proudfoot, J. G., Schuetzler, R. M., Elkins, A. C., Derrick, D. C. (2015). Robustness of multiple indicators in automated screening systems for deception detection. Journal of Management Information Systems, 32(4), 215-245. https://doi.org/10.1080/07421222.2015.1138569
Wahsheh, H., Al-Zahrani, M. (2021). Secure real-time computational intelligence system against malicious QR code links. International Journal of Computers Communications & Control, 16(3), 4186. https://doi.org/10.15837/ijccc.2021.3.4186
Wahsheh, H., Al-Zahrani, M., ...Oprea, M., Mihalache, S. F., Popescu, M. (2017). Computational intelligence-based PM2.5 air pollution forecasting. International Journal of Computers Communications & Control, 12(3), 365-380. https://doi.org/10.15837/ijccc.2017.3.2907
Walczyk, J. J., Igou, F. P., Dixon, A. P., Tcholakian, T. (2013). Advancing lie detection by inducing cognitive load on liars: a review of relevant theories and techniques guided by lessons from polygraph-based approaches. Frontiers in psychology, 4, 14. https://doi.org/10.3389/fpsyg.2013.00014
Wang, X., Shi, Y., Liu, L. (2021). Research on non-invasive psychological detection technology based on artificial intelligence. Academic Journal of Humanities & Social Sciences, 4(3). https://doi.org/10.25236/AJHSS.2021.040303
Widacki, J. (2020). A Half-Century of Experiences with the Polygraph. European Polygraph 14(1)51, 58-61. https://doi.org/10.2478/ep-2020-0011
Zafran, E. L., Stickle, J. R. (1984). Polygraphs in Employment: A State Survey. Clev. St. L. Rev., 33, 751.
Sinescu, C., Negrutiu, M., Pop, D., Cuc, L., DeSabata, A., Negru, R., Podoleanu, A. (2009, May). The importance of holograms in dentistry. In Holography: Advances and Modern Trends (Vol. 7358, pp. 138-147). SPIE. https://doi.org/10.1117/12.821545
Toader, C. S., Rujescu, C. I., Feher, A., Sălăşan, C., Cuc, L. D., Bodnár, K. (2023). Generation differences in the behaviour of household consumers in Romania related to voluntary measures to reduce electric energy consumption. Amfiteatru Economic, 25(64), 710-727. https://doi.org/10.24818/EA/2023/64/710
Additional Files
Published
Issue
Section
License
Copyright (c) 2025 Dana Rad, Csaba Kiss, Gavril Rad, Nicolae Paraschiv, Marius Bălaș
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
