Refining Local 3D Feature Matching through Geometric Consistency for Robust Biometric Recognition

Local features are gaining popularity due to their robustness to occlusion and other variations such as minor deformation. However, using local features for recognition of biometric traits, which are generally highly similar, can produce large numbers of false matches. To increase recognition performance, we propose to eliminate some incorrect matches using a simple form geometric consistency, and some associated similarity measures. The performance of the approach is evaluated on different datasets and compared with some previous approaches. We obtain an improvement from 81.60% to 92.77% in rank-1 ear identification on the University of Notre Dame Biometric Database, the largest publicly available profile database from the University of Notre Dame with 415 subjects.