METHOD AND SYSTEM FOR VALIDATING PERSONALIZED ACCOUNT IDENTIFIERS USING BIOMETRIC AUTHENTICATION AND SELF-LEARNING ALGORITHMS

• US 20140297528A1
• Filed: 06/24/2013
• Published: 10/02/2014
• Est. Priority Date: 03/26/2013
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: Garg, Shalin, Vallat, Sathish, Agrawal, Sachin Kumar
• Granted Patent: US 10,650,379 B2
• Time in Patent Office: Days
• US Class Current: 705/44
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06Q 20/40145 Biometric identity checks
• CPC Class Codes: G10L 17/24 the user being prompted to utter a password or a predefined phrase

Abstract

A system and method for biometric authentication of a user using a personalised identification and associated biometric data therewith is disclosed. In one embodiment, a plurality of personalised identifiers and biometric data may be captured from a number of users and stored in a repository as stored records. The process of authentication may be divided into two phases. In the first phase, either a speech recognition or character recognition process may be applied in order to determine the text spoken or written by the user. Subsequently a few records may be fetched from the repository on the basis of text mapping. In the second phase, biometric authentication may be performed by comparing the biometric sample with the stored biometric data corresponding to the fetched records to uniquely identify a single user. Further a machine learning technique may be applied in order to periodically refine a plurality of models stored in the repository.

Claims

1. A user-personalized identifying and authenticating method, comprising:

capturing, by a processor, at least one personalized identifier as a biometric sample;

converting, by the processor, the at least one biometric sample into one or more text-scripts;

retrieving, by the processor, a set of records, each record comprising a text, a golden copy of biometric templates, and a model associated with the golden copy of biometric templates, by matching the one or more text-scripts with the text in each record;

identifying a model set comprising one or more models associated with the records;

comparing, by the processor, a biometric template corresponding to the biometric sample with the golden copy of biometric templates of the records to generate a matching score for each model in the model set;

determining that a model in the model set has matching score greater than or equal to a pre-defined threshold score;

identifying, by the processor, a user uniquely using the model having matching score greater than or equal to the pre-defined threshold; and

providing an indication identifying the user.

2. The method of claim 1, further comprising:

determining that a number of models in the model set having matching score greater than or equal to the pre-defined threshold score is more than one;

prompting, by the processor, the user to provide a real-time biometric sample corresponding to a randomly generated text displayed for the user;

comparing, by the processor, a biometric template corresponding to the real-time biometric sample with the golden copy of biometric templates associated with the models from the model set having matching score greater than or equal to the pre-defined threshold score; and

re-calibrating, by the processor, the models having matching scores greater than or equal to the pre-defined threshold score using the biometric template corresponding to the real-time captured biometric sample.

3. The method of claim 1, wherein the personalized identifier is a variable length data selected from a group comprising a numeric data, an alphabetic data and combinations thereof.

4. The method of claim 3, wherein the personalized identifier is capable of being either written or spoken, and capable of being transformed from either image to text or speech to text, respectively.

5. The method of claim 1, wherein the biometric sample and the real-time biometric sample are in the form of speech or a handwriting signature.

6. The method of claim 1, wherein the captured personalized identifier is converted into the one or more text-scripts using speech-to-text conversion or Optical Character Recognition (OCR) techniques.

7. The method of claim 1, wherein the text is converted into a hash-code or Unicode data format of standard data size.

8. The method of claim 1, wherein said personalized identifier is captured including non-transcriptable sounds, and is correlated with the stored text after removing and suppressing the non-transcriptable sounds.

9. The method of claim 1, wherein the matching of the one or more text-scripts with the text is performed using at least one algorithm selected from a group comprising:

a semantic string matching, an approximate string matching, a fuzzy string matching, and combinations thereof.

10. The method of claim 2, wherein the model set is being created and re-calibrated using at least one machine learning algorithm selected from a group comprising:

a Decision tree learning, an Association rule learning, an Artificial neural network, Genetic programming, Inductive logic programming, Support vector machines, Clustering, Bayesian networks, Reinforcement learning, Representation learning, Similarity and metric learning, Sparse Dictionary Learning, and combinations thereof.

11. The method of claim 2, wherein the model is created and re-calibrated utilizing metadata extracted from the real-time biometric sample and the golden copy of biometric templates.

12. The method of claim 1, further comprising:

assigning, by the processor, at least one privilege level to the at least one personalized identifier in each record using one or more attributes.

13. The method of claim 12, wherein the one or more attributes are selected from a group comprising:

transaction location, transaction purpose, transaction limit, network type, duration of transaction, and combinations thereof.

14. A user-personalized identifying and authenticating system, comprising:

a processor; and

a memory coupled to the processor, wherein the processor is capable of executing instructions stored in the memory, the instructions comprising instructions for;

capturing at least one personalized identifier as at least one biometric sample;

converting the biometric sample into one or more text-scripts;

retrieving a set of records, each record comprising a text, a golden copy of biometric templates, and a model associated with the golden copy of biometric templates, by matching the one or more text-scripts with the text in each record;

identifying a model set comprising one or more models associated with the records;

comparing a biometric template corresponding to the biometric sample with the golden copy of biometric templates of the records to generate a matching score for each model in the model set;

determining that a model in the model set has matching score greater than or equal to a pre-defined threshold score;

identifying a user uniquely using the golden template using the model having matching score greater than or equal to the pre-defined threshold; and

providing an indication identifying the user.

15. The system of claim 14, the instructions further comprising instructions for:

determining that a number of models in the model set having matching score greater than or equal to the pre-defined threshold score is more than one;

prompting the user to provide a real-time biometric sample corresponding to a randomly generated text displayed for the user;

comparing a biometric template corresponding to the real-time biometric sample with the golden copy of biometric templates associated with the models from the model set having matching score greater than or equal to the pre-defined threshold score; and

re-calibrating the models having matching score greater than or equal to the pre-defined threshold score using the biometric template corresponding to the real-time captured biometric sample.

16. The system of claim 14, the instructions further comprising instructions for:

speech-to-text conversion or optical character recognition (OCR) for converting speech-to-text, image-to-text and hand-writing-to-text respectively.

17. The system of claim 14, the instructions further comprising instructions for:

validating the one or more generated text-scripts from the user.

18. The system of claim 15, the instructions further comprising instructions for:

assigning, by the processor, at least one privilege level to the at least one personalized identifier in each record.

19. A non-transitory computer-readable medium storing computer-executable user-personalized identifying and authenticating instructions, the instructions comprising instructions for:

capturing at least one personalized identifier as a biometric sample;

converting the at least one biometric sample into one or more text-scripts;

retrieving a set of records, each record comprising a text, a golden copy of biometric templates, and a model associated with the golden copy of biometric templates, by matching the one or more text-scripts with the text in each record;

identifying a model set comprising one or more models associated with the records;

comparing a biometric template corresponding to the biometric sample with the golden copy of biometric templates of the records to generate a matching score for each model in the model set;

determining that a model in the model set has matching score greater than or equal to a pre-defined threshold score;

identifying a user uniquely using the model having matching score greater than or equal to the pre-defined threshold; and

providing an indication identifying the user.

20. The medium of claim 19, the instructions further comprising instructions for:

determining that a number of models in the model set having matching score greater than or equal to the pre-defined threshold score is more than one;

prompting the user to provide a real-time biometric sample corresponding to a randomly generated text displayed for the user;

comparing a biometric template corresponding to the real-time biometric sample with the golden copy of biometric templates associated with the models from the model set having matching score greater than or equal to the pre-defined threshold score; and

re-calibrating the models having matching scores greater than or equal to the pre-defined threshold score using the biometric template corresponding to the real-time captured biometric sample.