Graphical Models for Predicting Missing or Incomplete Biographic Data in Biometric Records

In classical face recognition, an input probe image is compared against a gallery of labeled face images in order to determine its identity. In most applications, the gallery images (identities) are assumed to be independent of each other, i.e., the relationship between gallery images is not exploited during the face recognition process. We propose a graph-based approach in which gallery images are used to generate a powerful network structure where the nodes correspond to individual identities (and consist of face images as well as biographic attributes such as gender, ethnicity, name, etc.) and the edge weights define the degree of similarity between two such nodes. One application of the graph-based gallery is prediction of biographic attributes. We use of the graph structure to model the relationship between the biometric records in a database. We then show the benefits of such a graph in deducing the biographic labels of incomplete records, i.e., records that may have missing biographic data.