Patent ID: 7496509

Claim:
An apparatus for updating a biometric authentication arrangement, said apparatus comprising: an arrangement for accepting obsolete target models associated with at least one obsolete biometric authentication system substrate; and an arrangement for transforming the obsolete target models into updated target models associated with at least one new or revised biometric authentication system substrate; said arrangement for transforming being adapted to transform the obsolete target models into the updated target models via a model component mapping step using at least one Gaussian mixture component and auxiliary data associated with the obsolete target models, wherein the model component mapping step involves providing a new Maximum A-Posteriori estimate based on means, covariances, and other statistical parameters associated with the obsolete target models and in the context of parameters associated with the at least one new or revisited biometric authentication system substrate; wherein the biometric authentication arrangement involves at least one of: voice authentication; fingerprinting; face recognition; hand geometry; iris scanning and retina scanning; and wherein the component mapping step further comprises: (1) reconstructing obsolete sample means ({circumflex over (μ)} 0i ) of a target speaker from an obsolete model and an obsolete substrate using an adaptation formula comprising μ 0 ⁢ i = n i n i + r ⁢ μ ^ 0 ⁢ i + r n 1 + r ⁢ m 0 ⁢ i , wherein i is the Gaussian mixture component, r is a global relevance factor, n i is a vector softcount n i and m 0i is an obsolete universal background model mean: (2) calculating a set of posterior probabilities of Gaussian mixture component i for a new universal background model, accounting for an obsolete sample mean {circumflex over (μ)} j , using a second formula comprising γ ij = ⁢ Pr ⁡ ( i | μ ^ 0 ⁢ j ) = ⁢ π 1 ⁢ i ⁢ p 1 ⁢ i ⁡ ( μ ^ 0 ⁢ j ) ∑ N 1 k = 1 ⁢ π 1 ⁢ k ⁢ p 1 ⁢ k ⁡ ( μ ^ 0 ⁢ ⁢ j ) , where ⁢ ⁢ 1 ≤ i ≤ N 1 , 1 ≤ j ≤ N 0 , and wherein π 1i denotes a prior probability and p 1i (·) denotes an observation probability of Gaussian mixture component i of the new universal background model; (3) calculating new sample mean estimates on a new substrate using a third formula comprising μ ^ 1 ⁢ i = ∑ k = 1 N 0 ⁢ n k ⁢ γ ik ⁢ μ ^ 0 ⁢ k / ∑ k = 1 N 0 ⁢ n k ⁢ γ ik , where 1≦i≦N 1 , and wherein each Gaussian mixture component i is weighted by an original sample size n k attributed to each Gaussian mixture component i to reflect a natural proportionality of tile data; and (4) computing new mean parameters using another adaptation formula comprising: μ 1 ⁢ i = α i ⁢ μ ^ 1 ⁢ i + ( 1 - α i ) ⁢ m 1 ⁢ i α i = ∑ k = 1 N 0 ⁢ n k ⁢ γ ik / ( ∑ k = 1 N 0 ⁢ n k ⁢ γ ik + r ) , ⁢ where ⁢ ⁢ 1 < i < N .