Biometric Based User Authentication and Data Encryption

• US 20070174633A1
• Filed: 11/29/2006
• Published: 07/26/2007
• Est. Priority Date: 12/07/2004
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Vetro, Anthony, Martinian, Emin, Yedidia, Jonathan, Khisti, Ashish, Draper, Stark
• Granted Patent: US 7,779,268 B2
• Time in Patent Office: Days
• US Class Current: 713/186
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G06V 40/1353 Extracting features related to minutiae or pores
• CPC Class Codes: H04L 9/3231 Biological data, e.g. fingerprint, voice or retina network architectures or network communication protocols for supporting authentication of entities using biometrical features in a packet data network H04L63/0861
• CPC Class Codes: H04L 9/3236 using cryptographic hash functions

Abstract

Biometric parameters acquired from human forces, voices, fingerprints, and irises are used for user authentication and access control. Because the biometric parameters are continuous and vary from one reading to the next, syndrome codes are applied to determine biometric syndrome vectors. The biometric syndrome vectors can be stored securely while tolerating an inherent variability of biometric data. The stored biometric syndrome vector is decoded during user authentication using biometric parameters acquired at that time. The syndrome codes can also be used to encrypt and decrypt data.

Claims

1. A method securely storing biometric parameters in a database, comprising:

encoding enrollment biometric parameters of a user using a syndrome encoder to produce an enrollment syndrome vector;

applying a hash function to the enrollment biometric parameters to produce an enrollment hash; and

storing the enrollment syndrome vector and the enrollment hash in a database.

2. The method of claim 1, further comprising:

acquiring enrollment biometric data from a user; and

extracting the enrollment biometric parameters from the enrollment biometric data.

3. The method of claims 2, further comprising:

aligning the enrollment biometric data based on a coordinate system intrinsic to the enrollment biometric data.

4. The method of claim 2, in which the enrollment biometric data are acquired of a face.

5. The method of claim 2, in which the enrollment biometric data are acquired of speech.

6. The method of claim 2, in which the enrollment biometric data are acquired of a fingerprint.

7. The method of claim 2, in which the enrollment biometric data includes minutiae of a fingerprint.

8. The method of claim 7, in which the enrollment biometric parameters indicate positions of the minutiae.

9. The method of claim 8, in which the enrollment biometric parameters indicate orientations of the minutiae.

10. The method of claim 3, in which the aligning is according to a coordinate system associated with a fingerprint core point.

11. The method of claim 1, further comprising:

decoding the enrollment syndrome vector using a syndrome decoder and authentication biometric parameters to produce decoded biometric parameters;

applying the hash function to the decoded biometric parameters to produce at authentication hash; and

comparing the authentication hash and the enrollment hash to determine a similarity between the enrollment biometric parameters and the authentication biometric parameters.

12. The method of claim 1, further comprising:

acquiring authentication biometric data from a user; and

extracting the authentication biometric parameters from the authentication biometric data.

13. The method of claim 11, in which the syndrome decoder uses belief propagation.

14. The method of claim 11, in which the syndrome decoder is based on a measurement model that accounts for structured noise in a process for acquiring the biometric data.

15. The method of claim 1, in which a design of the syndrome encoder further comprises the steps of:

determining parameters of a source model and a channel model from training data; and

constructing the syndrome encoder according to the parameters and the source model and the channel model.

16. The method of claim 15, in which the channel model includes structured noise.