INTU-AI: Digitalization of Police Interrogation Supported by Artificial Intelligence
Source abstract
Abstract
Traditional police interrogation processes remain largely time-consuming and reliant on substantial human effort for both analysis and documentation. Intuition Artificial Intelligence (INTU-AI) is a Windows application designed to digitalize the administrative workflow associated with police interrogations, while enhancing procedural efficiency through the integration of AI-driven emotion recognition models. The system employs a multimodal approach that captures and analyzes emotional states using three primary vectors: Facial Expression Recognition (FER), Speech Emotion Recognition (SER), and Text-based Emotion Analysis (TEA). This triangulated methodology aims to identify emotional inconsistencies and detect potential suppression or concealment of affective responses by interviewees. INTU-AI serves as a decision-support tool rather than a replacement for human judgment. By automating bureaucratic tasks, it allows investigators to focus on critical aspects of the interrogation process. The system was validated in practical training sessions with inspectors and with a 12-question questionnaire. The results indicate a strong acceptance of the system in terms of its usability, existing functionalities, practical utility of the program, user experience, and open-ended qualitative responses.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Deception detection and forensic psychology · Digital and Cyber Forensics · Cybercrime and Law Enforcement Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal
Computer Science and Communication Research Centre, 2411-901 Leiria, Portugal
Instituto de Telecomunicações, 2411-901 Leiria, Portugal
Funding
Fundação para a Ciência e a Tecnologia, I.P. (FCT) · Scientific Employment Stimulus · European Union
References (56)
Show source-provided references
1. Application of AI in Everyday Life
2. Maqueda, D.C.M. (2025). The Data Revolution in Justice. World Dev., 186.10.1016/j.worlddev.2024.106834 ↗
3. AI-Driven Justice: Evaluating the Impact of Artificial Intelligence on Legal Systems
4. Understanding The Offender: Behavioral Evidence Analysis In Forensic Interrogation–Methodologies, Emerging Trends And Applications
5. Farber, H.B., and Vyas, A. (2025). Truth And Technology: Deepfakes in Law Enforcement Interrogations. SSRN Electron. J., Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5122595.10.2139/ssrn.5122595 ↗
6. (2025, July 31). Polícia Judiciária. Available online: https://www.policiajudiciaria.pt/.
7. (2025, June 14). Polícia Judiciária Militar, Available online: https://www.defesa.gov.pt/pt/defesa/organizacao/sc/pjm.
8. Accuracy of deception judgments10.1207/s15327957pspr1003_2 ↗
9. Kleinberg, B., and Verschuere, B. (2021). How humans impair automated deception detection performance. Acta Psychol., 213.10.1016/j.actpsy.2020.103250 ↗
10. Chkroun, M., and Azaria, A. (2024, January 28–30). Autonomous Agents for Interrogation. Proceedings of the 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI), Herndon, VA, USA.10.1109/ICTAI62512.2024.00102 ↗
11. Applications of AI-Enabled Deception Detection Using Video, Audio, and Physiological Data: A Systematic Review10.1109/ACCESS.2024.3462825 ↗
12. Lie detection: What works?10.1177/09637214231173095 ↗
13. Dow, D., Jeu, C., and Watkins, S. (2024). How Reliable are Polygraph Examinations in Criminal Investigations? An Empirical Assessment. SSRN Electron. J., Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5202561.10.2139/ssrn.5202561 ↗
14. Brennen, T., and Magnussen, S. (2022). The science of lie detection by verbal cues: What are the prospects for its practical applicability?. Front. Psychol., 13.10.3389/fpsyg.2022.835285 ↗
15. Constâncio, A.S., Tsunoda, D.F., Silva, H.d.F.N., Silveira, J.M.d., and Carvalho, D.R. (2023). Deception detection with machine learning: A systematic review and statistical analysis. PLoS ONE, 18.10.1371/journal.pone.0281323 ↗
16. Advancements and Challenges in Video-Based Deception Detection: A Systematic Literature Review of Datasets, Modalities, and Methods10.1109/ACCESS.2025.3533545 ↗
17. Patel, K., Airen, P., and Singh, S. (2025, January 7–8). Advance Deception Detection using Multi-Modal Analysis. Proceedings of the 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), Chennai, India.10.1109/RMKMATE64874.2025.11042349 ↗
18. Darwin, C. (1872). The Expression of the Emotions in Man and Animals, John Murray. Available online: https://www.gutenberg.org/ebooks/1227.10.1037/10001-000 ↗
19. Dalgleish, T., and Power, M. (2000). Handbook of Cognition and Emotion, John Wiley & Sons.10.1002/0470013494 ↗
20. Plutchik, R. (1980). Emotion. Emotion, A Psychoevolutionary Synthesis, Longman Higher Education.
21. Burgoon, J.K. (2018). Microexpressions are not the best way to catch a liar. Front. Psychol., 9.10.3389/fpsyg.2018.01672 ↗
22. Ekman, P. (2002). Telling Lies, WW Norton.
23. Carson, T.L. (2010). Lying and Deception: Theory and Practice, Oxford University Press.10.1093/acprof:oso/9780199577415.001.0001 ↗
24. Kulhman, M.S. (1980). Nonverbal communications in interrogations. FBI L. Enforc. Bull., 49.
25. DePaulo, B.M., Lindsay, J.J., Malone, B.E., Muhlenbruck, L., Charlton, K., and Cooper, H. (2003). Cues to deception. Psychol. Bull., 129.10.1037//0033-2909.129.1.74 ↗
26. Vrij, A. (2008). Detecting Lies and Deceit: Pitfalls and Opportunities, John Wiley & Sons.
27. Cutuli, D. (2014). Cognitive reappraisal and expressive suppression strategies role in the emotion regulation: An overview on their modulatory effects and neural correlates. Front. Syst. Neurosci., 8.10.3389/fnsys.2014.00175 ↗
28. Câmara Municipal de Vagos (2025, June 14). Gerar PDF Cartão Cidadão. Available online: https://www.cm-vagos.pt/cmvagos/uploads/document/file/4995/gerar_pdf_cartao_cidadao.pdf.
29. (2025, June 14). Código de Processo Penal. Diário da República, 1.ª Série—N.º 40/1987, 1987. Portugal. Available online: https://diariodarepublica.pt/dr/legislacao-consolidada/decreto-lei/1987-34570075.
30. OpenAI (2024, December 12). Whisper: Open-Source Speech Recognition. Available online: https://openai.com/index/whisper/.
31. Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I. (2023, January 23–29). Robust speech recognition via large-scale weak supervision. Proceedings of the International Conference on Machine Learning Research (PMLR), Honolulu, HI, USA.
32. Cristian, R. (2025, June 14). flan-t5-Portuguese-Small-Summarization. Available online: https://huggingface.co/rhaymison/flan-t5-portuguese-small-summarization.
33. PyMuPDF (2025, July 24). Welcome to PyMuPDF. Available online: https://pymupdf.readthedocs.io/en/latest/.
34. Linzaer (2025, January 02). Ultra-Light-Fast-Generic-Face-Detector-1MB. Available online: https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB.
35. Durai, P. (2024, November 10). Github—Facial Emotion Recognition. Available online: https://github.com/spmallick/learnopencv/tree/master/Facial-Emotion-Recognition.
36. Calabrés, E.H. (2025, June 14). wav2vec2-lg-xlsr-en-Speech-Emotion-Recognition. Available online: https://huggingface.co/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition.
37. Grosman, J. (2025, January 10). Hugging Face. Available online: https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english.
38. Kapase, A., Uke, N., Savant, J., Desai, M., Ghatage, S., and Rahangdale, A. (2024, January 23–24). “AffectAlchemy”: An Affective Dataset Based on Plutchik’s Psychological Model for Text-Based Emotion Recognition and its Analysis Using ML Techniques. Proceedings of the 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India.10.1109/ICCUBEA61740.2024.10775193 ↗
39. DeepL (2024, November 13). DeepL Translate. Available online: https://www.deepl.com/en/translator.
40. (2024, November 14). Deep-Translator. Available online: https://github.com/nidhaloff/deep-translator.
41. (2025, June 14). FastText: Library for Efficient Text Classification and Representation Learning. Facebook Open Source. Available online: https://fasttext.cc/.
42. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv.
43. Kapase, A. (2025, August 04). Affect Alchemy: A Dataset for Plutchik-Based Emotion Recognition. Available online: https://github.com/ajaykapase/affect-alchemy.
44. Invideo AI (2024, December 26). 2024. Available online: https://invideo.io/.
45. Garcia, J.G. (2025, July 31). Video Corpus for Transcription Testing in the INTU-AI Project. Available online: https://drive.google.com/drive/folders/1xgyHinmyZtYfSghvwBGJwI-Jdt-jPaag?hl=pt-br.
46. kl3z (2025, July 31). INTU-IA Multimodal Approach. Available online: https://github.com/kl3z/INTU-IA-Multimodal_Aproach.
47. Niesen, J. (2025, July 31). Jiwer: Speech-Text Evaluation Measures in Python. Available online: https://pypi.org/project/jiwer/.
48. A Sentença (2025, July 31). TVI. Available online: https://www.imdb.com/title/tt32119132/.
49. OpenAI (2025, June 14). ChatGPT (GPT-4o). Available online: https://openai.com/chatgpt.
50. DeepSeek (2025, June 14). DeepSeek-V2 Language Model. Available online: https://deepseek.com/.
51. Lin, C.Y. (2004, January 25–26). Rouge: A package for automatic evaluation of summaries. Proceedings of the Text Summarization Branches Out, Barcelona, Spain.
52. INTU-AI Project (2025, July 31). INTU-AI—Intuition Artificial Intelligence. Available online: https://drive.google.com/drive/folders/1U1xONIXLtSniIxZP386VrekKvyF0x-FZ?hl=pt-br.
53. TypeForm (2025, July 31). PJM IA Evaluation Questionnaire. Online Questionnaire for Qualitative Evaluation of PJM IA Software. Available online: https://form.typeform.com/to/YGw8ayvZ.
54. Likert, R. (1932). A Technique for the Measurement of Attitudes. Arch. Psychol., 22.
55. User Acceptance of Information Technology: Toward a Unified View10.2307/30036540 ↗
56. META (2025, June 10). Llama 4. Available online: https://www.llama.com/.