User-Assisted People Search in Consumer Image Collections

In this paper, we investigate the process of searching for images of specified people in the consumer family photo domain. This domain is very different from the controlled environment of secure-access applications that have been extensively studied and face recognition packages are available in the market. Instead of the typical frontal mug shot, consumer photos are more likely to show people with unconstrained pose and illumination. This domain is also unique in that there are a large number of instances of a limited number of unique individuals. We develop and test facial recognition that is specifically targeted to this domain, using facial features that are derived from active shape modeling of faces, followed by a combination of the features using AdaBoost. We also provide a workflow that is suitable for lay users, and which rewards user inputs with improved performance. Test results show good performance on a challenging data set of consumer images.