We study two-stage search procedures for biometric identification systems in an information-theoretical setting. Our main conclusion is that clustering based on vector-quantization achieves the optimum trade-off between the number of clusters (cluster rate) and the number of individuals within a cluster (refinement rate). The notion of excess rate is introduced, a parameter which relates to the amount of clusters to which the individuals belong. We demonstrate that noisier observation channels lead to larger excess rates. © 2009 IEEE.
|Title of host publication
|2009 IEEE International Symposium on Information Theory, ISIT 2009, 28 June 2009 through 3 July 2009, Seoul
|Published - 2009