Latent fingerprints are, by definition, left unintentionally and are obtained using diverse physical and chemical techniques. It follows that the initial fingerprint image quality can be very poor. As such, these images are unsuitable for feature marking and entry into database and identification software. To address this problem forensics analysts use image enhancement to amplify the fingerprint image quality and to diminish obscuring features. This process is critical to subsequent analysis, and “before and after” comparisons are striking for the information that image enhancement process reveals. Nevertheless, while considerable research investigates new chemical and physical techniques to obtain images and analyzes fingerprint marking and identification algorithms, analysis of the intermediate image enhancement process generally has been overlooked. Our research will focus on this under explored link in forensic analysis with a goal to increase its systematic characterization and reproducibility.
Image enhancements performed for latent fingerprint analysis
Image metrics and well-defined algorithms will be researched and developed to analyze and quantify the image transformation processes applied by forensic scientists in the context of latent fingerprint analysis. The research will focus on two principal aspects.
Image Enhancement Tools
The research and demonstration of capabilities described herein is significant because it will give forensic analysts the ability to quantify evidence that is currently analyzed qualitatively and minimize the reliance on subjective judgment during examination. Currently many prints that could be enhanced through software are not analyzed or compared because they are deemed “of no value”. Furthermore, due to the lack of quantitative techniques for image enhancement many forensic laboratories currently do not employ or allow image enhancement software. The proposed research will provide techniques and processes to potentially enable forensic labs and latent fingerprint examiners to analyze and compare more evidence.
End Date:on going
Lead Organizational Unit:itl
Andrew Dienstfrey, Mathematical Analysis and Modeling
David Witzke, Foray Technologies