Efficacy of direct heat application for latent fingerprint development on thermal paper : A comparative quality assessment between experts and artificial intelligence
Keywords:
Latent fingerprints, Thermal paper, Heating element, Artificial intelligence, Forensic scienceAbstract
This study aimed to evaluate the effectiveness of direct heat application for developing latent fingerprints on thermal paper and to compare fingerprint quality assessments between forensic experts and artificial intelligence (ChatGPT). A total of 80 thermal paper samples, including automated teller machine (ATM) receipts and point-of-sale (POS) receipts, were examined. Latent fingerprints were developed using two heat-based methods: a hot plate (100 °C) and an iron (70-100 °C) under controlled experimental conditions. The results indicated that the hot plate method produced slightly higher average fingerprint quality scores and demonstrated greater consistency than the iron method. In addition, ATM thermal paper yielded slightly higher fingerprint quality than POS thermal paper. Comparative evaluation revealed that ChatGPT provided scores comparable to those of forensic experts, with no statistically significant difference (p = 0.56). The agreement analysis demonstrated a Weighted Cohen’s Kappa value of 0.62 indicating a substantial level of agreement that is academically acceptable. These findings suggest that artificial intelligence has potential as a supportive tool for latent fingerprint assessment in forensic science. Nevertheless, in-depth analysis and courtroom-admissible interpretation still necessitate expert judgment.
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