Surface Classification and Detection of Latent Fingerprints: Novel Approach Based on Surface Texture Parameters

The classification of surfaces has importance in the field of latent fingerprint detection in crime scene forensics because the adaptive preprocessing depends on the surface texture. This paper shows a novel approach to classify various materials with the help of surface texture parameters. Three-dimensional surface data of potential crime scene is produced using a chromatic white light sensor. The results are classified and used to separate fingerprints from carrier surfaces, all in focus of the working field of forensics and latent fingerprint detection. So far, the use of surface roughness is mainly known within the framework of material quality control. A meaningful and useful classification of different surfaces, as required in this project, is not existent. This paper describes a concept, which is novel in the field of criminalistic forensics using knowledge from surface appearance and a chromatic white light sensor. In our experiments forty surface parameters are defined and used to classify ten different materials in this test set-up. Five material classes are created. Further it is shown in first experiments, that some surface texture parameters are sensitive to categorize if a scan include fingerprints or not.