System for determining biometric representation data

A system comprising:

• one or more memory devices storing first computer-executable instructions;
• and
• one or more hardware processors to execute the first computer-executable instructions to:
• determine trained model data, wherein:
• the trained model data is associated with first training feature data corresponding to two or more types of features of an object depicted in first training input image data,
• the trained model data is associated with operation of multiple portions of a first machine learning network (“first network”), and
• the trained model data is based on:
• a first loss value that is associated with first intermediate data that corresponds to the two or more types of features of the object,
• a second loss value that is associated with first representation data that is based on the first training input image data, and
• a third loss value that is based at least on the first loss value and the second loss value;
• receive first input image data;
• determine second representation data, based on using the first network and the trained model data to process the first input image data;
• determine enrolled user data comprising previously stored representation data that is associated with identification data;
• and
• determine asserted identification data based on a comparison of the second representation data and the previously stored representation data.

A method comprising:

• determining a first set of training data comprising first training input image data, associated first training feature data, and associated label data, wherein:
• the associated first training feature data comprises at least:
• first information associated with a first type of feature of an object depicted in the first training input image data, and
• second information associated with a second type of feature of the object depicted in the first training input image data;
• providing the first training input image data as input to a first machine learning network (“first network”);
• determining, based on the first training input image data and using a first portion of the first network, first intermediate data, wherein the first intermediate data corresponds to respective types of feature data;
• determining, based on the first intermediate data and the associated first training feature data, a first loss value using a first loss function;
• determining, based on the first training input image data and using a second portion of the first network, first representation data;
• determining, based on the first representation data and the associated label data, a second loss value using a second loss function;
• determining a first weight associated with the first loss value;
• determining a second weight associated with the second loss value;
• determining a third loss value based on the first loss value, the first weight, the second loss value, and the second weight;
• determining trained model data based at least in part on the third loss value, wherein the trained model data is associated with operation of the first portion and the second portion of the first network;
• acquiring second input image data;
• and
• determining second representation data, based on using the first network and the trained model data to process the second input image data.