Iris Biometrics: Indexing and Retrieving Heavily Degraded Data

Most of the methods to index iris biometric signatures were designed for decision environments with a clear separation between genuine and impostor matching scores. However, in case of less controlled data acquisition, images will be degraded and the decision environments poorly separated. This paper proposes an indexing/retrieval method for degraded images and operates at the code level, making it compatible with different feature encoding strategies. Gallery codes are decomposed at multiple scales, and according to their most reliable components at each scale, the position in an n-ary tree determined. In retrieval, the probe is decomposed similarly, and the distances to multiscale centroids are used to penalize paths in the tree. At the end, only a subset of the branches is traversed up to the last level. When compared with related strategies, the proposed method outperforms them on degraded data, particularly in the performance range most important for biometrics $({\rm h\it~rates}>{\rm 0.95})$ . Finally, according to the computational cost of the retrieval phase, the number of enrolled identities above which indexing is computationally cheaper than an exhaustive search is determined.