A data association approach to detect and organize people in personal photo collections

In this paper we present a method to automatically segment a photo sequence in groups containing the same persons. Many methods in literature accom- plish to this task by adopting clustering techniques. We model the problem as the search for probable associations between faces detected in subsequent photos con- sidering the mutual exclusivity constraint: a person can not be in a photo two times, nor two faces in the same photo can be assigned to the same group. Associations have been found considering face and clothing descriptions. In particular, a two level architecture has been adopted: at the first level, associations are computed within meaningful temporal windows (situations); at the second level, the resulting clusters are re-processed to find associations across situations. Experiments confirm our technique generally outperforms clustering methods. We present an analysis of the results on a public dataset, enabling future comparison, and on private collections.