An on-line learning method for face association in personal photo collection

Due to the widespread use of cameras, it is very common to collect thousands of personal photos. A proper organization is needed to make the collection usable and to enable an easy photo retrieval. In this paper, we present a method to organize personal photo collections based on “who” is in the picture. Our method consists in detecting the faces in the photo sequence and arranging them in groups corresponding to the probable identities. This problem can be conveniently modeled as a multi-target visual tracking where a set of on-line trained classifiers is used to represent the identity models. In contrast to other works where clustering methods are used, our method relies on a probabilistic framework; it does not require any prior information about the number of different identities in the photo album. To enable future comparison, we present experimental results on a public dataset and on a photo collection generated from a public face dataset.

► Faces in a photo collection are detected and grouped in an unsupervised manner.
► The identity models are estimated on-line via discriminative methods.
► A probabilistic framework enforces the mutual exclusivity constraint among faces detected in the same photo.
► The effectiveness of the proposed method is demonstrated with experimental results.