Abstract: An example method includes obtaining, by processing circuitry, an image depicting a face of a person wearing eyewear and a reflection pattern generated by projecting light onto a lens of the eyewear and determining, by the processing circuitry, an optical parameter of the lens based on the image. The method further includes receiving, by the processing circuitry and from an electronic database, a personal characteristic data relating to a previously identified person and comparing, by the processing circuitry, the optical parameter of the lens to the personal characteristic data. The method further includes determining, by the processing circuitry and based on the comparison, a score indicative of a match between the person wearing eyewear and the previously identified person.

Abstract: An example method includes obtaining, by processing circuity, an image depicting a face of a person wearing eyewear and a reflection pattern generated by projecting light onto a lens of the eyewear and determining, by the processing circuitry, an optical parameter of the lens based on the image. The method further includes receiving, by the processing circuitry and from an electronic database, a personal characteristic data relating to a previously identified person and comparing, by the processing circuitry, the optical parameter of the lens to the personal characteristic data. The method further includes determining, by the processing circuitry and based on the comparison, a score indicative of a match between the person wearing eyewear and the previously identified person.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive anomaly detection. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive anomaly detection by utilizing at least one of defined interaction level anomaly scores, such as defined interaction level anomaly scores for non-constant defined interaction levels that are determined using weighted feature tuple anomaly scores for feature tuple values that are associated with the non-constant defined interaction levels, as well as defined interaction level anomaly scores for constant defined interaction levels that are determined using an anomaly distribution measure for an anomaly quantization metric across a plurality of inferred predictive entities.

Abstract: Embodiments herein relate to viral transfer risk management. In example embodiments, an apparatus is configured to retrieve a first user identifier associated with a first client computing entity. The apparatus is further configured to determine, based at least in part on a first transfer risk score associated with the first user identifier, a first transfer risk score grouping for the first user identifier. The apparatus is further configured to, based at least in part on the first transfer risk score grouping and physical space parameters associated with a physical space identifier, allocate a first physical space assignment within a physical space associated with the physical space identifier to the first user identifier associated with the first client computing entity.

Abstract: Embodiments herein relate to viral transfer risk management. In example embodiments, an apparatus is configured to retrieve a first user identifier associated with a first client computing entity. The apparatus is further configured to determine, based at least in part on a first transfer risk score associated with the first user identifier, a first transfer risk score grouping for the first user identifier. The apparatus is further configured to, based at least in part on the first transfer risk score grouping and physical space parameters associated with a physical space identifier, allocate a first physical space assignment within a physical space associated with the physical space identifier to the first user identifier associated with the first client computing entity.