3. The method as recited in claim 1 , further comprising: training a plurality of cluster classifiers for each image cluster of the plurality of image clusters, each of the plurality of cluster classifiers being associated with a given image feature; and determining a probability that the unknown person instance is each known person instance in the image cluster based on a given image feature by using the associated cluster classifier.
1. A method of identifying people in digital images using cluster-based person recognition comprising: dividing, by at least one processor, images of an image gallery into a plurality of image clusters, each image cluster comprising a plurality of images from the image gallery that share one or more common characteristics, the one or more characteristics comprising one or more characteristics other than identities of the plurality of person instances in the images; training, by the at least one processor, a cluster classifier for each image cluster of the plurality of image clusters based on a plurality of known person instances; determining, by the at least one processor, an image cluster of the plurality of image clusters to which an image comprising an unknown person instance most corresponds based on one or more characteristics of the image and common characteristics of the determined image cluster of the plurality of image clusters, the one or more characteristics of the image comprising one or more characteristics other than an identity of the unknown person instance; identifying, by the at least one processor, a plurality of context cues between the unknown person instance and the plurality of known person instances or between the plurality of known person instances; and determining, by the at least one processor, a probability that the unknown person instance is each known person instance in the determined image cluster using the cluster classifier of the determined image cluster and the identified plurality of context cues.