Clustering Consumer Photos Based on Face Recognition

The ability of finding photos of a particular person through face recognition is a highly desired feature in indexing, searching and browsing consumer photo collections. In this research, based on an advanced face recognition engine we developed in prior work, one two-pass clustering approach is proposed which groups photos of the same person in a fully automatic way. Firstly, a similarity matrix for all detected faces is computed, with which a semi-supervised clustering is done. Next, larger clusters are selected and modeled as people frequently appearing in the image collection. Then, smaller clusters are recognized against these dominant clusters. Contextual information is used to obtain better results. The approach achieved promising accuracy when tested on an image dataset containing 2316 photos.