Though speed and accuracy are two competing requirements for large scale biometric recognition, they both suffer from large database size. Clustering seems promising to reduce the search space. This can improve accuracy, but may even contrarily affect it by a poor selection of the candidate cluster for the search. We present a novel technique that exploits gallery entropy for clustering. The comparison with K-Means demonstrates that we achieve a better clustering result, yet without fixing the number of clusters a-priori.
Entropy based biometric template clustering / M., Nappi; D., Riccio; DE MARSICO, Maria. - STAMPA. - (2013), pp. 560-563. (Intervento presentato al convegno 2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013 tenutosi a Barcelona nel 15 February 2013 through 18 February 2013) [10.5220/0004266205600563].
Entropy based biometric template clustering
DE MARSICO, Maria
2013
Abstract
Though speed and accuracy are two competing requirements for large scale biometric recognition, they both suffer from large database size. Clustering seems promising to reduce the search space. This can improve accuracy, but may even contrarily affect it by a poor selection of the candidate cluster for the search. We present a novel technique that exploits gallery entropy for clustering. The comparison with K-Means demonstrates that we achieve a better clustering result, yet without fixing the number of clusters a-priori.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
