Iris crypts: Multi-scale detection and shape-based matching

This paper presents an improved framework for iris crypt detection and matching that outperforms both previous methods and manual annotations. The system uses a multi-scale pyramid architecture to detect feature candidates before they are further examined and optimized by heuristic-based methods. The dissimilarity between irises are measured by a two-stage matcher in the simple to complex order. The first stage estimates the global dissimilarity and rejects the majority of unmatching candidates. The surviving pairs are matched by local dissimilarities between each crypt pair using shape descriptors. The proposed framework showed significant performance improvement in both identification and verification context.