Biometric identification systems and methods

1. A method of identity verification using biometric signature data, comprising:

creating a face sample database based on a plurality of acquired face samples, each of the plurality of acquired face samples including parameters for defining different postures and expressions;

calculating a feature database by extracting selected features of entries in the face sample database;

calculating positive samples and negative samples based on entries in the feature database and feature absolute value distances;

calculating a key bin feature using a learning algorithm, the key bin feature distinguishing each of the positive samples and negative samples; and

calculating a classifier from the key bin feature for use in identifying and authenticating an acquired face image of a person-to-be-identified,

wherein calculating positive samples comprises calculating a feature absolute value distance for a same position of any two different images from one person, and

wherein calculating negative samples comprises calculating a feature absolute value distance for a same position of different people.

2. The method of claim 1 , wherein calculating a feature database comprises calculating at least one of local binary pattern features and local ternary pattern features from entries in the face sample database.

3. The method of claim 1 , further comprising using the classifier to create a private key associated with an intended recipient of a data message in a data encryption system.

4. The method of claim 1 , wherein the learning algorithm is an adaptive boosting learning algorithm.

5. The method of claim 1 , further comprising:

receiving a face image of the person-to-be-identified;

extracting at least one feature from the face image; and

using the classifier to determine the identity of the person-to-be-identified.

6. A system for identity verification using biometric signature data, the system comprising:

a processor; and

a memory storing logical instructions that, when executed by the processor, are configured to:

create a face sample database based on a plurality of acquired face samples, each of the plurality of acquired face samples including parameters for defining different postures and expressions;

calculate a feature database by extracting selected features of entries in the face sample database and feature absolute value distances;

calculate positive samples and negative samples based on entries in the feature database;

calculate a key bin feature using a learning algorithm, the key bin feature distinguishing each of the positive samples and negative samples; and

calculate a classifier from the key bin feature for use in identifying and authenticating an acquired face image of a person-to-be-identified,

wherein the positive samples are calculated by calculating a feature absolute value distance for a same position of any two different images from one person, and

wherein the negative samples are calculated by calculating a feature absolute value distance for a same position of different people.

7. The system of claim 6 , wherein the feature database is calculated by calculating at least one of local binary pattern features and local ternary pattern features from entries in the face sample database.

8. The system of claim 6 , wherein the logical instructions are further configured to use the classifier to create a private key associated with an intended recipient of a data message in a data encryption system.

9. The system of claim 6 , wherein the learning algorithm is an adaptive boosting learning algorithm.

10. The system of claim 6 , wherein the logical instructions are further configured to:

receive a face image of the person-to-be-identified;

extract at least one feature from the face image; and

use the classifier to determine the identity of the person-to-be-identified.

11. A method of verifying an identity of a person-to-be-identified using biometric signature data, the method comprising:

creating a sample database based on biometric signature data from a plurality of individuals;

calculating a feature database by extracting selected features from entries in the sample database;

calculating positive samples and negative sampled based on entries in the feature database and feature absolute value distances;

calculating a key bin feature using an adaptive boosting learning algorithm, the key bin feature distinguishing each of the positive samples and negative samples; and

calculating a classifier from the key bin feature for use in identifying and authenticating a person-to-be-identified,

wherein the positive samples are calculated by calculating a feature absolute value distance for a same position of any two different images from one person, and

wherein the negative samples are calculated by calculating a feature absolute value distance for a same position of different people.

12. The method of claim 11 , further comprising using the classifier to create a private key associated with an intended recipient of a data message in a data encryption system.

13. The method of claim 11 , further comprising:

receiving a first set of biometric signature data of the person-to-be-identified at a first location;

extracting at least one feature from the biometric data of the person-to-be-identified; and

using the classifier to determine the identity of the person-to-be-identified at the first location.

14. The method of claim 13 , wherein the first set of biometric signature data of the person-to-be-identified is chosen based on a desired security level of authentication.

15. The method of claim 13 , further comprising:

receiving the first set of biometric signature data of the person-to-be-identified at a second location;

extracting at least one feature from the biometric data of the person-to-be-identified; and

using the classifier to determine the identity of the person-to-be-identified at the second location.

16. The method of claim 5 wherein the classifier is one of a left eye classifier, right eye classifier, left eye coarse detection classifier, right eye coarse detection classifier or a left eye and right eye classifier.