Automated facial recognition library construction with privacy protection

A client device configured for automated construction of an anonymized facial recognition library, the client device comprising:

• a camera;
• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the client device to carry out operations including:
• with the camera, capturing a first plurality of images of faces of members of a panel of viewers of media content presented on a content presentation device collocated with the client device during one or more viewing sessions carried out over a particular time interval;
• applying a first machine learning (ML) model implemented on the client device to the first plurality to generate a second plurality of feature vectors, each associated with a different one of the images of the faces, wherein the first ML model is trained to extract facial-image feature vectors from images that contain one or more faces;
• computationally determining one or more clusters of feature vectors of the second plurality within a vector space of the feature vectors;
• for each respective cluster in the vector space, determining a respective centroid feature vector marking the center of the respective cluster, and respectively assigning a unique cluster identifier (ID) to the respective centroid feature vector, wherein the anonymized facial recognition library comprises the respective centroid feature vectors and their respectively assigned unique cluster IDs;
• and
• determining a respective association between each cluster ID and a respective name ID based on panel-member information received at the client device, wherein each respective name ID is unique to a different one of the panel members, and wherein each respective association associates one of the respective name IDs with one or more cluster IDs.

A non-transitory computer-readable medium having instructions stored thereon that, when carried out by one or more processors of a client device having a camera, cause the client device to carry out operations including:

• with the camera, capturing a first plurality of images of faces of members of a panel of viewers of media content presented on a content presentation device collocated with the client device during one or more viewing sessions carried out over a particular time interval;
• applying a first machine learning (ML) model implemented on the client device to the first plurality to generate a second plurality of feature vectors, each associated with a different one of the images of the faces, wherein the first ML model is trained to extract facial-image feature vectors from images that contain one or more faces;
• computationally determining one or more clusters of feature vectors of the second plurality within a vector space of the feature vectors;
• for each respective cluster in the vector space, determining a respective centroid feature vector marking the center of the respective cluster, and respectively assigning a unique cluster identifier (ID) to the respective centroid feature vector, wherein the anonymized facial recognition library comprises the respective centroid feature vectors and their respectively assigned unique cluster IDs;
• and
• determining a respective association between each cluster ID and a respective name ID based on panel-member information received at the client device, wherein each respective name ID is unique to a different one of the panel members, and wherein each respective association associates one of the respective name IDs with one or more cluster IDs.