Polarization- and Specular-Reflection-Based, Non-contact Latent Fingerprint Imaging and Lifting

In forensic science the finger marks left unintentionally by people at a crime scene are referred to as latent fingerprints . Most existing techniques to detect and lift latent fingerprints require application of certain material directly onto the exhibit. The chemical and physical processing applied onto the fingerprint potentially degrades or prevents further forensic testing on the same evidence sample. Many existing methods also come with deleterious side effects. We introduce a method to detect and extract latent fingerprint images without applying any powder or chemicals on the object. Our method is based on the optical phenomena of polarization and specular reflection together with the physiology of fingerprint formation. The recovered image quality is comparable to existing methods. In some cases like the sticky side of a tape our method shows unique advantages

Image enhancement for improving visibility and feature recognition

Researchers analyze images in areas such as geology, bat cardiovascular systems and art studies to verify their observations. Some images are hard to study as their details are not vivid; hence there is a need to enhance these images to facilitate their study while preserving their contents. This study is aimed at assisting the researchers in the Cardiovascular Systems Dynamic Laboratory at Texas A&M University by evaluating the importance of Image Enhancement (IE) for improving visibility of features. For this study the images were collected and manipulated using various IE techniques and were shown to the novice researchers who were asked to perform three different tasks. These tasks were representative of the research work conducted in the lab. The techniques that were selected aimed at reducing the problems that are usually associated with data obtained from microscopic feeds. A customized application was developed to expedite and automate the study. The results indicated that the researchers did not immmensely benefit from the improved visualization for easy tasks. However, their performance improved for tasks that required more practice and skill. Our approach contributes towards designing an effective training program for novice researchers in the lab. Moreover, it is promising for similar research in different fields of study