| 摘要 |
Clustering biometric templates is performed by determining fiduciary templates and cluster seed templates, both from a gallery of biometric templates. Similarity vectors are formed by comparing members of the cluster seed templates to the fiduciary templates. The gallery is then partitioned into clusters based upon the similarity vectors, and the clusters are populated from the remainder of the gallery. Partitioning may be performed by a classifier that implements a supervised machine learning algorithm that is trained with the similarity vectors, such as a multi-decision tree classification system. Matching may be accommodated by accessing a probe template, determining a cluster neighborhood for the probe template, and searching the cluster neighborhood to determine whether the gallery includes a match corresponding to the probe template. The same similarity metric is used both to partition a gallery into clusters, and in matching a probe template to the so-clustered gallery.
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