HomePsychology and Education: A Multidisciplinary Journalvol. 49 no. 5 (2025)

Fingerprint Identification Practices Among Law Enforcement Agencies: A Mixed-Method Study

Rogelio Abarte Jr | Lina Azuelo

Discipline: Law

 

Abstract:

This mixed-methods explanatory sequential study was conducted to determine fingerprint identification practices among law enforcement agencies on Panay Island for the calendar year 2024-2025. The respondents for the quantitative part of the study were one hundred (100) purposively selected respondents, composed of ninety (90) PNP Investigators, eight (8) PNP fingerprint experts, and two (2) NBI fingerprint experts, classified according to their length of service and course. For the qualitative part, the participants were the eight (8) fingerprint experts of the PNP and two (2) fingerprint experts of the NBI, who hold the designation of fingerprint examiner and have five years or more of experience in fingerprint identification. The respondents were classified according to their length of service and the course in which they were enrolled. The extent of fingerprint identification practices among law enforcement agencies was determined using a researcher-developed questionnaire that was duly validated by experts. The mean, frequency, percentage, and standard deviation were used for descriptive data analysis, while the Mann-Whitney test was used for inferential data analysis; the level of significance was set at 0.05. Thematic analysis was employed to interpret the qualitative data. The findings indicated that fingerprint identification practices among law enforcement personnel were moderate in terms of length of service and course. There was no significant difference in fingerprint identification practices among law enforcement personnel, regardless of length of service or course of study. Four key themes emerged in the representation of fingerprint identification practices across different law enforcement agencies: the use of standardized processes, the use of the ACE-V method, the use of methods and techniques for fingerprint comparison, and the conduct of fingerprint verification. The moderate practice of fingerprint identification among law enforcement personnel across all categories, including length of service and course, provided a baseline level of competency in fingerprint identification practices within the law enforcement community. The lack of significant differences in fingerprint identification practices among law enforcement personnel, when classified by length of service and course, suggests that factors such as experience and academic background do not significantly affect these practices. The key themes identified in the interviews, including the use of standardized processes in fingerprint identification, the application of the ACE-V method, the utilization of methods and techniques for fingerprint comparison, and the conduct of fingerprint verification, highlighted the core practices and methods employed by various law enforcement agencies in fingerprint identification. This study has shed light on the intricate and methodical practices that underpin fingerprint identification across various law enforcement agencies on Panay Island. The emergence of four key themes reflects a shared commitment among these agencies to uphold the accuracy and reliability of fingerprint examinations. Furthermore, the study highlights the importance of ongoing professional development, the reinforcement of standardized procedures, and the enhancement of forensic competencies to ensure that fingerprint identification remains a credible and indispensable tool in the pursuit of justice.



References:

  1. Badua (2016). Dactyloscopy Manual and Workbook, Wiseman's Books Trading, Inc.
  2. Champod C. (2015). Fingerprint identification: advances since the 2009 National Research Council report. Phil. Trans. R. Soc. B 370: 20140259. http://dx.doi.org/10.1098/rstb.2014.0259
  3. Chen, R., Liu, S., & Wong, T. (2020). Standardized protocols and their impact on forensic performance in fingerprint identification. Journal of Police Science and Management, 18(4), 321–338.
  4. Cole, S. (2016). Scandal, fraud, and the reform of forensic science: the case of fingerprint analysis, 119 W. Va. L. Rev.. from https://researchrepository.wvu.edu/wvlr/vol119/iss2/5
  5. Cooks, A. (2022). What is cognitive dissonance and how do you reduce it. BetterUp. from https://www.betterup.com/blog/cognitive-dissonance
  6. Creswell et al. (2003). research design, qualitative, quantitative and mixed methods approaches, second edition. Sage Publications, Inc.
  7. Delizo, D. (2023). Unpublished training module in forensic personal identification.
  8. Depayso, V. (2018). The basics of fingerprint palmprint & footprint, Wiseman's Books Trading, Inc.
  9. Eldridge, S. (2023). Cognitive bias. Encyclopaedia Britannica. From https://www.britannica.com/science/cognitive-bias
  10. Federal Bureau of Investigation (1987), The science of fingerprint. US Government Printing Office. Fingerprint Analysis. http://dx.doi.org/10.1016/j.forsciint.2016.08.026https://researchrepository.wvu.edu/wvlr/vol119/iss2/5
  11. Festinger, L. (1957). Cognitive dissonance theory: a review. Theory Hub. from https://open.ncl.ac.uk/theories/7/cognitive-dissonance-theory/
  12. Gao, Q. & Pinto, D. (2016). Some challenges in forensic fingerprint classification and interpretation. from https://www.researchgate.net/publication/304189718
  13. Garcia, J., Santos, M., & Reyes, P. (2018). The impact of professional development workshops on fingerprint identification skills among law enforcement officers. Journal of Forensic Science and Practice, 14(2), 123–135.
  14. Gibb, C. & Riemen, J. (2023). Toward better AFIS practice and process in the forensic fingerprint environment. Forensic Science International: Synergy. from https://doi.org/10.1016/j.fsisyn.2023.100336
  15. Gillis, A. (2024). What is cognitive bias?. TechTarget, Search Enterprise. From https://www.techtarget.com/searchenterpriseai/definition/cognitive-bias
  16. Innovatrics (2024). What is fingerprint identification? from https://www.innovatrics.com/glossary/fingerprintidentification/#:~:text=Fingerprint%20identification%20is%20a%20multifaceted,present%20on%20an%20individual,'s%20fingertips   .
  17. Jiang, X. (2015), Fingerprint classification. In: Li SZ and Jain AK (Eds.), Encyclopedia of biometrics: 584-592. Springer Science and Business Media, Berlin, Germany. from https://doi.org/10.1007/978-1-4899-7488-4_56
  18. Kadane, J. (2018). Fingerprint science. The Annals of Applied Statistics 2018, Vol. 12, No. 2, 771–787 . from https://doi.org/10.1214/18-AOAS1140
  19. Kaushal N. (2011). Human Identification and Fingerprints: A Review. Journal of Biometrics and Biostatics. From DOI: 10.4172/2155-6180.1000123
  20. Kern, J. (2017). Fingerprinting: a study in cognitive bias and its effects on latent fingerprint analysis. Undergraduate Honors College Theses 2016-. 32. from https://digitalcommons.liu.edu/post_honors_theses/32
  21. Pacheco, I., Cerchiai, B., & Stoiloff, S. (2014). Miami-Dade research study for the reliability of the ACE-V process: accuracy & precision in latent fingerprint examinations. from https://www.ojp.gov/pdffiles1/nij/grants/248534.pdf
  22. Petrovic N. (2022). Automated Fingerprint Identification System: with and without the Possibility of Correction of a Digitalised Image. ResearchGate. From https://doi.org/10.17559/TV-20200625120039
  23. Rocamora, J. (2023). Fingerprint: uniqueness and persistency of friction ridge skin. Wiseman's Books Trading, Inc.
  24. Rossman, G. & Wilson, B. (1985). Combining quantitative and qualitative methods in a single large-scale evaluation study. Sage Journals. from https://doi.org/10.1177/0193841X8500900505
  25. Smith, A., & Jones, B. (2017). Professional development and forensic science practices: Bridging the gap in evidence collection. Law Enforcement Research Journal, 12(4), 89–101.
  26. Stevenage, S. & Pitfield, C. (2016). Fact or friction: examination of the transparency, reliability and sufficiency of the ACE-V method of fingerprint analysis. Forensic Science International 267 (2016) 145–156. From http://dx.doi.org/10.1016/j.forsciint.2016.08.026
  27. Stickel, L. (2016). The unwritten laws of american fingerprinting. J Civil Legal Sci 5: 210. from doi: 10.4172/2169-0170.1000210
  28. Sumad-on, D. T. and Cawi, R. D.  (2022). The methods of extracting trace evidence in criminal investigation. International Journal of Innovative Science and Research Technology. ISSN No:-2456-2165. From https://www.academia.edu/116214317/The_Methods_of_Extracting_Trace_Evidence_in_Criminal_Investigation
  29. Swietkowiak M. (2025). Reliability of fingerprint experts in extracting and evaluating minutiae in individualization tests of fingerprint traces. Journal of Forensic and Legal Medicine. From https://doi.org/10.10116/j.jflm.2025.102943
  30. Thales Group (2024). What is ACE-V? from https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/biometrics/biometric-software/ace-v
  31. Trapecar M. (2022). Short study of using ACE-V and GYRO for fingerprint examination and individualization. Research Journal of Science and Technology. from DOI: 10.53022/oarjst.2022.5.2.0060
  32. US Department of Justice (2021). Law enforcement. Bureau of Justice and Statistics. From https://bjs.ojp.gov/topics/lawenforcement#:~:text=Law%20enforcement%20describes%20the%20agencies,individuals%20suspected%20of%20criminal%20offenses