Abstract: Methods and systems for detecting fraudulent activity at a self-checkout terminals of a retail store include a scanner for scanning an identifier of a candidate product located in the product-scanning area of the self-checkout terminal, and one or more sensors that detect at least one physical characteristic of the candidate product located in the product-scanning area of the self-checkout terminal. A computing device then correlates the obtained electronic data corresponding to actual identifying characteristic information associated with the candidate product to the reference physical characteristic information associated with the reference product in order to generate a similarity score between the actual and reference physical characteristic information.

Abstract: A control circuit accesses a digitized image of a particular item and then processes that digitized image to thereby assign various aspects of the digitized image to various dimensions in N-dimensional Euclidean space to thereby provide an N-dimensional representation. The control circuit then accesses a database containing a plurality of various clustered groups of N-dimensional representations. At least some of the clustered groups have a corresponding identifying label. Conducting a nearest neighbor search serves to identify a clustered group to which the particular item most likely belongs to thereby provide an identified cluster group. When the identified cluster group does not have a corresponding identifying label, the control circuit can then further determine whether a predetermined condition has also been met. When true, the control circuit alerts a user via a user interface that the identified cluster group should be assigned an identifying label.