Image analysis – Applications – Personnel identification
Reexamination Certificate
1998-07-31
2003-06-17
Ahmed, Samir (Department: 2623)
Image analysis
Applications
Personnel identification
C340S005520, C704S238000
Reexamination Certificate
active
06580814
ABSTRACT:
BACKGROUND
1. Technical Field
The present application relates generally to biometric systems and, in particular, to a system and method for building compressed biometric models for each enrolled user in a biometric system, whereby the compressed biometric models are stored in an engine database of the biometric system rather than full biometric models.
2. Description of Related Art
Conventional biometric systems generally operate by storing full biometric models (e.g. codebooks) for each enrolled user of the system (i.e., the entire population of persons to be recognized by the biometric system). These models can be built, for example, from statistical data such as Gaussian distribution data which is computed from a collection of feature vectors that are generated during a biometric feature extraction process. The conventional biometric systems generally perform user identification or verification by comparing the distances between a temporary biometric model (or feature vectors), which is generated for an individual making an identity claim, with training models of enrolled users (that are previously built and stored during an enrollment process) and finding the training model having the shortest distance from the temporary biometric model (or feature vectors).
The problem with these conventional biometric systems, however, is that the storage requirements for the biometric training models becomes significant when the system is trained to recognize and verify a large population. There is a need, therefore, for a system and method for building compressed biometric models for enrolled users which reduce the storage requirements of the biometric system without affecting or reducing the ability of the biometric system to perform accurate biometric identification/verification.
SUMMARY
The present application is directed to a system and method for building compressed biometric models. A compressed biometric model for each enrolled user is constructed from rank and distance parameters which are derived by computing the distance between a temporary biometric model (which is built from biometric data provided by the user) and a plurality of biometric reference models which are stored in the engine database of the biometric system. The plurality of biometric reference models consist of a set of conventional biometric models (i.e., not compressed) for a given number L of randomly chosen individuals, which are generated prior to user enrollment. The L reference models are randomly divided into M subsets.
During enrollment, a temporary biometric model of a given user is compared with the reference models in each of the M subsets so as to score rank and distance values. The rank and distance parameters are used to build the compressed biometric models in accordance with the following model:
&Sgr;
I
(
M
j
, R
i
, D
(
M
j
, R
i
))={[
I
(
M
1
, R
1
, D
(
M
1
, R
1
)), . . . ,
I
(
M
1
, R
i
, D
(
M
1
, R
i
))], . . . , [
I
(
M
j
, R
1
, D
(
M
j
, R
1
)), . . . ,
I
(
M
j
, R
i
, D
(
M
j
, R
i
))]}
where I represents the identity of the closest reference model in a corresponding subset M
j
; R
i
refers to the ranking of the closeness of the reference model to the temporary biometric model as compared with the closeness of each of the other reference models in the corresponding subset M
j
; and D refers to the corresponding distance measure between the reference model and the temporary biometric model.
The compressed biometric models are then stored in the engine database rather than storing the full (i.e., temporary biometric models) that are initially created during user enrollment. Consequently, by not having to store the full biometric models for each enrolled user, the storage requirements of the biometric system may be significantly reduced.
REFERENCES:
patent: 5787394 (1998-07-01), Bahl et al.
patent: 5799088 (1998-08-01), Raike
patent: 5812739 (1998-09-01), Hirayama
patent: 6073096 (2000-06-01), Gao et al.
patent: 6073101 (2000-06-01), Maes
patent: 2-162400 (1990-06-01), None
patent: 02-162400 (1990-06-01), None
Ittycheriah Abraham P.
Maes Stephane H.
Ahmed Samir
F. Chau & Associates LLP
International Business Machines - Corporation
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