ABSTRACT
On recordings of certain crimes, the face is not always shown. In such cases, hands can offer a solution, if they are completely visible. An important aspect of this study was to develop a method for hand comparison. The research method was based on the morphology, anthropometry, and biometry of hands. A new aspect of this study was that a manual and automated test were applied, which, respectively, assess many features and provide identification rates quickly. An important observation was that good quality images can provide sufficient hand details. The most distinctive features were the length/width ratio, the palm line pattern and the quantity of highly distinctive features present, and how they are distributed. The results indicate that experience did not improve the identification rates, while the manual test did. Intra-observer variability did not influence the results, whereas hands of relatives were frequently misjudged. Both tests provided high identification rates.
Subject(s)
Anthropometry , Forensic Sciences , Hand/anatomy & histology , Algorithms , Checklist , Databases, Factual , Female , Humans , Image Processing, Computer-Assisted , Male , Professional Competence , Skin Pigmentation , White PeopleABSTRACT
In this research, we examined whether fixed pattern noise or more specifically Photo Response Non-Uniformity (PRNU) can be used to identify the source camera of heavily JPEG compressed digital photographs of resolution 640 x 480 pixels. We extracted PRNU patterns from both reference and questioned images using a two-dimensional Gaussian filter and compared these patterns by calculating the correlation coefficient between them. Both the closed and open-set problems were addressed, leading the problems in the closed set to high accuracies for 83% for single images and 100% for around 20 simultaneously identified questioned images. The correct source camera was chosen from a set of 38 cameras of four different types. For the open-set problem, decision levels were obtained for several numbers of simultaneously identified questioned images. The corresponding false rejection rates were unsatisfactory for single images but improved for simultaneous identification of multiple images.
