System, Method, and Computer Program Product for Evolutionary Learning in Verification Template Matching During Biometric Authentication

A method, comprising:

• training, with at least one processor, a first machine learning model based on a training dataset of a plurality of images of one or more first users, wherein the first machine learning model is configured to authenticate an identity of the one or more first users based on an input image of the one or more first users;
• generating, with at least one processor, a plurality of image feature templates using the first machine learning model, wherein each image feature template of the plurality of image feature templates is associated with a positive authentication of the identity of the one or more first users during a time interval;
• generating, with at least one processor, a second machine learning model based on the plurality of image feature templates;
• generating, with at least one processor, a predicted image feature template using the second machine learning model;
• determining, with at least one processor, whether to authenticate the identity of a second user based on an input image of the second user, wherein
• determining whether to authenticate the identity of the second user based on the input image of the second user comprises:
• generating a current image feature template based on the input image of the second user;
• comparing the current image feature template to the predicted image feature template;
• and
• determining whether the current image feature template corresponds to the predicted image feature template;
• and
• performing an action based on determining whether to authenticate the identity of the second user.

A computer program product, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to:

• train a first machine learning model based on a training dataset of a plurality of images of one or more first users, wherein the first machine learning model is configured to authenticate an identity of the one or more first users based on an input image of the one or more first users;
• generate a plurality of image feature templates using the first machine learning model, wherein each image feature template of the plurality of image feature templates is associated with a positive authentication of the identity of the one or more first users during a time interval;
• generate a second machine learning model based on the plurality of image feature templates;
• generate a predicted image feature template using the second machine learning model;
• determine to authenticate the identity of a second user based on an input image of the second user, wherein, the one or more instructions that cause the at least one processor to determine to authenticate the identity of the second user based on the input image of the second user, cause the at least one processor to:
• generate a current image feature template based on the input image of the second user,
• compare the current image feature template to the predicted image feature template, and
• determine that the current image feature template corresponds to the predicted image feature template;
• and
• perform an action based on determining whether to authenticate the identity of the second user.