Issues in Rotational (Non-)invariance and Image Preprocessing

Lalit Jain, Michael J. Wilber, Terrance E. Boult; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 76-83

This paper addresses two problems that have been largely overlooked in the literature. First, many systems seek to use, and algorithms claim to provide, rotational in-variance, such as fingerprint minutiae or SIFT/SURF features. We introduce a statistical test for rotational independence, using lossless rotations to show the differences are statistically significant and cannot be attributed to image noise. We use this to experimentally show fingerprint feature extractors fail to be rotation independent. We show the popular "rotation invariant" SURF and SIFT feature extractors, used in both biometric and general vision, also fail the rotation independence test. We then introduce a match-twist-match (MTM) paradigm and experimentally demonstrate that, by reducing the effective angular difference between probe and gallery, we can improve system matching performance. Our analysis, using FVC2002 and FVC2004 datasets, further shows that differences in extracted features impact the overall system performance of fingerprint matching of both matchers tested. Using the MTM approach, we reduce our secure template system's errors by 10%-20% helping us to define the current state of the art in the FVC-OnGoing Secure template competition with an EER of 1.698%. We end by bringing to the forefront the growing danger of sensors over-preprocessing of images. We show examples of the problems that can arise with preprocessing. As our rotation experiments showed, the impact of even modest numbers of feature errors suggest these preprocessing issues are likely very significant. We suggest the need for policy guidelines that require disclosure of preprocessing steps used and the development of standards for testing the impact of preprocessing.