Method and system for validating personalized account identifiers using biometric authentication and self-learning algorithms

A computer implemented method for identifying and authenticating at least one user by means of at least one personalized identifier, the method comprising:

• storing, by a processor, a first set of records associated with the at least one user, each record comprising a text, a golden copy of biometric templates and a model associated with the golden copy of biometric templates, wherein the text, the golden copy of biometric templates and the model are associated with the at least one personalized identifier;
• assigning, by the processor, at least one privilege level to the at least one personalized identifier in each record of the first set of records based on one or more attributes;
• capturing, by the processor, the at least one personalized identifier in the form of a first real time biometric sample wherein the first real time biometric sample is in the form of speech or a handwriting signature;
• converting, by the processor, the first real time biometric sample into one or more text-scripts;
• retrieving, by the processor, a second set of records from the first set of records, by matching the one or more text-scripts with the stored text in each record of the first set of records and accordingly identify a model set comprising one or more models associated with each record from the first set of records and the second set of records;
• comparing, by the processor, a biometric template corresponding to the biometric sample with the golden copy of biometric templates of each of the records from the second set of records to generate a matching score, wherein the matching score is generated for each model in the model set;
• identifying, by the processor the at least one user uniquely from the model set having the matching score greater than or equal to a pre-defined threshold, when the number of models in the model set having the matching score greater than or equal to a pre-defined threshold score is more than one;
• prompting, by the processor, the at least one user to provide a second real time biometric sample corresponding to a randomly generated text displayed to the at least one user, wherein the second real time biometric sample is in the form of a speech or a handwriting signature;
• comparing, by the processor, a biometric template corresponding to the second real time biometric sample with the golden copy of biometric templates associated with the models from the model set, having the matching score greater than or equal to a pre-defined threshold score to uniquely identify the at least one user;
• re-calibrating, by the processor, the model set having the matching score greater than or equal to a pre-defined threshold score using the biometric template corresponding to the second real-time captured biometric sample, wherein the model set is being created and recalibrated using at least one machine learning technique selected from a group comprising at least one of a decision tree learning, an association tree learning, and an Artificial neural network based on the second set of records;
• and
• generating, by the processor, a user dependent model using the at least one machine learning technique and metadata of the at least one user, wherein the metadata is used for identification of the at least one user from a set of users.