SURF and MU-SURF descriptor comparison with application in soft-biometric tattoo matching applications

Abstract

In this work a comparison of the SURF and MUSURF feature descriptor vectors is made. First, the descriptors’ performance is evaluated using a standard data set of general transformed images. This evaluation consists in counting correspondences and correct matches between ten image pairs. Image pairs have different transformations (rotation, scale change, viewpoint change, blur, JPEG compression and illumination change) in order to evaluate the descriptors in different environments. The second test evaluates the descriptors’ suitability for tattoo matching. In this case, one hundred randomly chosen transformed tattoo images are matched against a database of ten thousand images. The transformations include rotation change, RGB noise and cropped images. Non-transformed images are also evaluated. In both tests, the descriptors represent the interest points previously detected and stored into a file by the same detector, to ensure the validity of the test. Results show that the newer and modified version of the SURF descriptor, MU-SURF, performs better than its counterpart and it is suitable for tattoo matching.