Biometric recognition using a classification neural network
First Claim
1. A biometric recognition system for authenticating biometric indicia of a user, the system comprising:
- data storage means for storing a plurality of master pattern sets, each master pattern set corresponding one of a plurality of authorized users, each of the master pattern sets defined by a plurality of master features and master orientation data of the plurality of master features;
vector generation means for producing a comparison vector representing the level of similarity between a master pattern set and the biometric indicia; and
a neural network for producing classification designators based on the comparison vector, wherein the classification designators are indicative of whether the user'"'"'s biometric indicia should be authenticated;
wherein the vector generation means comprises;
identification means for identifying sample features in the biometric indicia that best match each of the master features;
pattern generation means for generating sample orientation data based on the sample features that best match each of the master features; and
means for comparing the master orientation data and the sample orientation data to produce comparison orientation data;
wherein the comparison vector is based on the comparison orientation data and is also based on the similarity of the master features and their corresponding sample features; and
the master pattern sets are derived using the following equation;
##EQU5## where S is the set of all m-by-m features in an image, with the exception of R, which is a reference feature;
Rij is an (i, j)th pixel gray level in feature R;
Iij i s an (i, j)th pixel gray level in I; and
R and I are mean gray levels within the respective features.
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Abstract
A biometric recognition system involves two phases: creation of a master pattern set of authorized users biometric indicia and authentication using a classification neural network. To create the master pattern set, an image of an authorized biometric user'"'"'s indicia is divided into a plurality of regions of interest or "features". The system determines which features are the most useful for identification purposes. Master patterns are then created from these master features, thus creating a master pattern set. During authentication, a sample pattern set of a user to be authenticated is similarly created. A neural network is used to compare the sample pattern set with the master pattern set to determine whether the user should be authenticated.
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Citations
23 Claims
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1. A biometric recognition system for authenticating biometric indicia of a user, the system comprising:
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data storage means for storing a plurality of master pattern sets, each master pattern set corresponding one of a plurality of authorized users, each of the master pattern sets defined by a plurality of master features and master orientation data of the plurality of master features; vector generation means for producing a comparison vector representing the level of similarity between a master pattern set and the biometric indicia; and a neural network for producing classification designators based on the comparison vector, wherein the classification designators are indicative of whether the user'"'"'s biometric indicia should be authenticated; wherein the vector generation means comprises; identification means for identifying sample features in the biometric indicia that best match each of the master features; pattern generation means for generating sample orientation data based on the sample features that best match each of the master features; and means for comparing the master orientation data and the sample orientation data to produce comparison orientation data; wherein the comparison vector is based on the comparison orientation data and is also based on the similarity of the master features and their corresponding sample features; and the master pattern sets are derived using the following equation;
##EQU5## where S is the set of all m-by-m features in an image, with the exception of R, which is a reference feature;
Rij is an (i, j)th pixel gray level in feature R;
Iij i s an (i, j)th pixel gray level in I; and
R and I are mean gray levels within the respective features. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for generating a master pattern set of a biometric indicia of a user, comprising the steps of:
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(a) acquiring an image of the biometric indicia to produce a biometric pattern image, wherein the biometric pattern image includes a plurality of features; (b) comparing each feature in the biometric pattern image with all other features in the biometric pattern image; (c) assigning a uniqueness value to each feature in the biometric pattern image based on the results of comparing step (b); (d) choosing, based on the uniqueness values, from the features in the biometric pattern image a plurality of master features; (e) defining master patterns based on the master features; (f) storing the master patterns and master features as the master pattern set, wherein assigning step (c) uses the following equation;
##EQU7## where S is the set of all m-by-m features in an image, with the exception of R, which is a reference feature;
Rij is an (i, j)th pixel gray in feature R;
Iij is an (i, j)th pixel gray level in I; and
R and I are mean gray levels within the respective features. - View Dependent Claims (14, 15, 16, 17)
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18. A method for authenticating a biometric pattern of a user, in which authorized biometric patterns are stored and represented by corresponding master pattern sets comprised of a plurality of master patterns based on a plurality of master features in the corresponding biometric pattern, comprising the steps of:
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(a) acquiring an image of a biometric pattern to be authenticated and producing therefrom a sample pattern image, wherein the sample pattern image includes a plurality of sample features; (b) retrieving a master pattern set; (c) comparing each master feature in the retrieved master pattern set with the sample features and determining therefrom which sample feature best matches each master feature, and producing therefrom a set of matched sample features; (d) defining sample patterns based on the set of matched sample features; (e) comparing the sample patterns with the master patterns; (f) authenticating the sample pattern image upon a favorable comparison step (e), wherein comparing step (c) uses the following equation;
##EQU8## where S is the set of all m-by-m features in an image, with the exception of R, which is a reference feature;
Rij is an (i, j)th pixel gray level in feature R;
Iij is an (i, j)th pixel gray level in I; and
R and I are mean gray levels within the respective features. - View Dependent Claims (19, 20, 21, 22)
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23. A method for authenticating a biometric pattern of a user, comprising the steps of:
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storing a master pattern set from the user, the master pattern set defined by a plurality of master features and master orientation data of the plurality of master features; acquiring a sample biometric pattern of a user to be authenticated; identifying sample features in the sample biometric pattern that best match each of the plurality of master features; generating sample orientation data based on a pattern generated by the identified sample features; comparing the master orientation data and the sample orientation data to produce comparison orientation data; producing a comparison vector based on the similarity of the plurality of master features and their corresponding identified sample features and based on the comparison orientation data; producing classification designators based on the comparison vector; and authenticating the biometric pattern if the classification designators indicate a match, wherein the comparing step uses the following equation;
##EQU9## where S is the set of all m-by-m features in an image, with the exception of R, which is a reference feature;
Rij an (i, j)th pixel gray level in feature R;
Iij is an (i, j)th pixel gray level in I; and
R and I are mean gray levels within the respective features.
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Specification