Automatic tattoo image registration system

Surveillance systems are very important for law enforcement and military applications. Capturing a biometric modality at a distance and under difficult conditions is a very challenging process. While face or gait can be used to identify an individual in such application, tattoos can also help in the identification process whenever available. Tattoos are considered a soft biometric and in some scenarios may be the only clue that can be used to verify the identity of a suspect or to rule out a suspect. One of the major challenges in tattoo recognition systems is image registration, i.e. the alignment of one tattoo image to a reference image. Accurate registration can greatly improve recognition accuracy. In this paper, we propose a two-level automatic tattoo registration and correction system based on SIFT descriptors and the RANSAC algorithm with a homography model. By using image quality index techniques and a postprocessing step (where we refine our original registration results by an automated correction process where outliers are first identified and then re-processed), our system is able to demonstrate accurate registration results. We tested our registration system using two tattoo image databases. The first one is the NIST-Tatt-C database with 109 subjects collected under uncontrolled condition, and the second one is the new WVU tattoo database (WVU-Tatt) with 79 subjects, which is collected under controlled conditions. Experimental results show that, first, we obtained 100% registration accuracy in both databases. Then, the effect of our registration process on tattoo recognition performance was assessed when using both the NIST-Tatt-C database where the accuracy improved from 54.13% (no registration) to 100% (with registration) and the WVU-Tatt database where the accuracy improved from 86.08% (no registration) to 98.73% (with registration).