Book Title: Modern Forensic Tools and Devices: Trends in Criminal Investigation
Editors: Mr. Ravi Kumar, Ms. Nandini Katare, Don Caeiro, and Dr. Surbhi Girdhar
Chapter: 4
DOI: https://doi.org/10.59646/658/4
Author: Hiramanee Patidar
Abstract
The analysis of fingerprints has long been a cornerstone of forensic science, with expert testimony often presented in court based on the comparison of latent prints from crime scenes to known prints of defendants. This long-standing practice is rooted in the perceived permanence and uniqueness of individual fingerprints, leading to a historical belief in the infallibility of such evidence. However, the advent of Automated Fingerprint Identification Systems has revolutionized this process, transitioning from purely manual, labor-intensive comparisons to sophisticated electronic and automated methods (Jain & Pankanti, 2001). This evolution has significantly enhanced the efficiency and speed of fingerprint identification, particularly in searching vast databases of exemplars, thereby mitigating the laborious nature of manual examination. Despite their advanced capabilities, AFIS primarily functions as a search system in forensic contexts, generating candidate matches for human examiners to ultimately verify, especially concerning latent prints which are often distorted or of lower quality (Gibb & Riemen, 2023; Ulery et al., 2014). Prior to the development of biometric technology, the consolidation of criminal records, including ten-print identification, was exclusively performed through manual methods. This reliance on manual examination often involved a single examiner, though the overall production process typically included multiple individuals, such as detectives providing suspect prints. The scientific validity of fingerprint evidence, once considered unassailable, has recently come under increased scrutiny, moving away from claims of “zero error rates” to a more empirically driven assessment of accuracy. This reevaluation highlights the imperative to understand the scientific foundations and limitations of AFIS technology, moving beyond popular misconceptions often portrayed in media, where identifications are erroneously depicted as fully automatic (Langenburg et al., 2015). Instead, the process involves intricate human-computer interaction, where the AFIS provides a ranked list of potential matches, and trained latent print examiners then perform detailed comparisons to render a definitive conclusion.