Context-inclusive face clustering

Abstract

People represented in multiple images can be recognized using accurate facial similarity metrics, where the accuracy can be further improved using contextual information. A set of models can be trained to process image data, and facial features can be extracted from a face region of an image and passed to the trained models. Resulting feature vectors can be concatenated and the dimensionality reduced to generate a highly accurate feature vector that is representative of the face in the image. The feature vector can be used to locate similar vectors in a multi-dimensional vector space, where similarity can be determined based at least in part upon the distance between the endpoints of those vectors in the vector space. Context information from the image can be used to adjust the similarity determination. Similar vectors can be clustered together such that the faces represented by those images are associated with the same person.