Incorporating Graph-Based Models in a Deep Learning Framework for Operational Face Recognition

Organizing a biometric gallery into a graph

Collaborator: Arun Ross
Sponsor: National Institute of Justice Graduate Research Fellowship

Project Description

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. This network can be used in several different ways: (a) to create clusters of identities based on graph clustering algorithms; (b) to predict the biographic and demographic attributes of an unknown probe image based on label propagation schemes; (c) to perform rapid recognition by restricting the search to only a fraction of the nodes in the graph; and (d) to infer missing information in nodes based on adjacent nodes that have strong edges.

Biographic Prediction

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. In particular, we use a label propagation scheme to deduce missing values for both binary-valued biographic attributes (e.g., gender) as well as multi-valued biographic attributes (e.g., age group). Experimental results using face-based biometric records consisting of name, age, gender and ethnicity convey the pros and cons of the proposed method.

Publications

Paper

Code

Data

T. Swearingen and A. Ross. "Label propagation approach for predicting missing biographic labels in face-based biometric records," IET Biometrics. 2017.

Paper

Code

Data

T. Swearingen and A. Ross, "Predicting Missing Demographic Information in Biometric Records using Label Propagation Techniques," Proc. of the 15th International Conference of the Biometrics Special Interest Group (BIOSIG 2016), (Darmstadt, Germany), September 2016.