Myths about fingerprint evidence: Basic facts countering miscarriage of justice. Part 1

The long practice of fingerprint science is accompanied by confusing thoughts affecting the interpretation of the fingerprint evidence recovered from a crime scene, and, consequently, prosecutors and judges’ decisions as well. However, despite the tremendous scientific and technological developments relating to fingerprint enhancement, processing, and usage, which clarify precise facts regarding the influence of deposition circumstances, substrate, light, air, temperature, and time factors on fingerprint secretions, misconceptions about fingerprints are still widespread within the law enforcement and judicial system. This problem prevents the proper usage of fingerprints in fighting crimes and supporting the justice system by strong physical evidence. This study aims to highlight some scientific facts about fingerprints as well as a new approach and reconceptualization of fingermarks as a tool for crime scene investigation and training. The article discusses twenty-four myths about fingerprints – part 1 covers ten of them and part 2 discusses the other fourteen.

Latent Fingerprint Enhancement Based on EDTV Model

The chapter presents latent fingerprint enhancement technique for enforcement agencies to identify criminals. There are many challenges in the area of latent fingerprinting due to poor-quality images, which consist of unclear ridge structure and overlapping patterns with structure noise. Image enhancement is important to suppress several different noises for improving accuracy of ridge structure. The chapter presents a combination of edge directional total variation model, EDTV, and quality image enhancement with lost minutia re-construction, RMSE, for evaluation and performance in the proposed algorithm. The result shows the average of three different image categories which are extracted from the SD7 dataset, and the assessments are good, bad, and ugly, respectively. The result of RMSE before and after enhancement shows the performance ratio of the proposed method is better for latent fingerprint images compared to bad and ugly images while there is not much difference with performance of bad and ugly.