Face Track Recognition with Multi-sample Multi-view Weighting

摘要

In one embodiment, a method determines known features for existing face tracks that have identity labels and builds a database using these features. The face tracks may have multiple different views of a face. Multiple features from the multiple faces may be taken to build the face models. For an unlabeled face track without identity information, the method determines its sampled features and finds labeled nearest neighbor features with respect to multiple feature spaces from the face models. For each face in the unlabeled face track, the method decomposes the face as a linear combination of its neighbors from the known features from the face models. Then, the method determines weights for the known features to weight the coefficients of the known features. Particular embodiments use a non-linear weighting function to learn the weights that provides more accurate labels.

主权项

1. A method comprising:
determining, by a computing device, a plurality of face tracks for detected faces in a video; receiving, by the computing device, a set of labels for a portion of the plurality of face tracks from a set of users to form a set of labeled face tracks, wherein a label in the set of labels identifies an identity for a face track in the set of labeled face tracks; extracting, by the computing device, a first set of features for an unlabeled face track in the plurality of face tracks, wherein an identity for the unlabeled face track is not known; correlating, by the computing device, the extracted first set of features for the unlabeled face track to a second set of features from the set of labeled face tracks; generating, by the computing device, feature weights for the first set of features based on confidence scores for the second set of features using a weighting function that magnifies feature weights for the second set of features with higher confidence scores and suppresses feature weights for the second set of features with lower confidence scores in a non-linear manner; and using, by the computing device, the generated feature weights to determine a label for the unlabeled face track by applying the generated feature weights to the second set of features.