Creating feature based image recognition subclasses for identity verification

A method comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• receiving a plurality of facial scans of an individual;
• training, using the plurality of facial scans, a convolutional neural network (CNN) to identify the individual based on a first facial scan of the individual, wherein training the CNN comprises:
• converting the plurality of facial scans to a frequency domain,
• identifying a center of gravity for each of the plurality of facial scans, and
• generating, based on distances between the centers of gravity, one or more subclasses for the plurality of facial scans, wherein each subclass is defined by a center of gravity range limited by a minimum center of gravity value and a maximum center of gravity value;
• receiving an authorization request including the first facial scan of the individual;
• inputting the first facial scan into the CNN, wherein inputting the first facial scan into the CNN causes the CNN to:
• converting the first facial scan to the frequency domain,
• identifying a center of gravity for the first facial scan,
• comparing the center of gravity for the first facial scan to center of gravity ranges to identify a corresponding subclass, and
• identifying, based on the corresponding subclass, the individual;
• based on successful identification of the individual, granting requested access to the individual;
• and
• updating, using the first facial scan, the CNN.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• receive a plurality of facial scans of an individual;
• train, using the plurality of facial scans, a convolutional neural network (CNN) to identify the individual based on a first facial scan of the individual, wherein training the CNN comprises:
• converting the plurality of facial scans to a frequency domain,
• identifying a center of gravity for each of the plurality of facial scans, and
• generating, based on distances between the centers of gravity, one or more subclasses for the plurality of facial scans, wherein each subclass is defined by a center of gravity range limited by a minimum center of gravity value and a maximum center of gravity value;
• receive an authorization request including the first facial scan of the individual;
• input the first facial scan into the CNN, wherein inputting the first facial scan into the CNN causes the CNN to:
• convert the first facial scan to the frequency domain,
• identify a center of gravity for the first facial scan,
• compare the center of gravity for the first facial scan to center of gravity ranges to identify a corresponding subclass, and
• identify, based on the corresponding subclass, the individual;
• based on successful identification of the individual, grant requested access to the individual;
• and
• update, using the first facial scan, the CNN.