Uncovering vein patterns from color skin images for personal identification in forensic investigation

Abstract

Recent technological advances have allowed for a proliferation of digital evidence images. Using these images as evidence in legal cases (e.g. child sexual abuse, child pornography and masked gunmen) can be very challenging, because the faces of criminals or victims are not visible. Although large skin marks and tattoos have been used, they are ineffective in some legal cases, because the skin exposed in evidence images have neither unique tattoos nor enough skin marks for identification. The blood vessel between the skin and the muscle covering most parts of the human body is a powerful biometric trait, because of its universality, permanence and distinctiveness. Traditionally, it was impossible to use vein patterns for forensic identification, because they were not visible in color images. All the current vein recognition systems developed by companies and research laboratories rely on near infrared (NIR) imaging devices to capture high quality vein patterns from hand and wrist, where skin is relatively thin, for commercial applications. Up until now, no one studies vein patterns for forensic identification because no method has been developed to visualize vein patterns hidden in color images. The primary aim of this research is to develop algorithms for visualizing vein patterns hidden in color images so that criminal and victim identification can be performed based on vein patterns. The secondary aim of this research is to develop algorithms for removing JPEG blocking artifacts in skin images that adversely affect forensic recognition. We propose two approaches for uncovering vein patterns from color skin images. The first approach is based on RGB-NIR mapping. It extracts information from a pair of synchronized color and NIR images and uses a neural network (NN) to map RGB values to NIR intensities. Furthermore, we design an automatic intensity adjustment scheme for illumination compensation and an NN weight adjustment scheme for improving the robustness of the approach. Using an automatic matching algorithm, we match resultant images from the RGB-NIR mapping approach and find that its matching result is comparable to the result from matching NIR images, which are always considered as ground truth of vein patterns. I owe a great deal of appreciation and gratitude to other members in our research team, Arfika, Xiaojie, Rubha, Quang, Xingpeng, Yanzhu, Frodo, Abhik, Han and Hamid. I am so lucky to have been able to work with you all. My thanks go in particular to Hamid for his valuable help and suggestions in the procedure of submitting thesis. I am especially indebted to all my Chinese friends for providing such a caring and fun-filled environment. I want to thank present and past members of the Forensic and Security Laboratory: Raj, Dilip, Pravin, Atiqur, Weihua, Alex, Than and Deepak. Our talks and discussions on research and other random topics have been an enjoyable and unforgettable experience for me. Words fail me when I try to express my appreciation to my family. I extend warm thanks to my parents and p...