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                            Funding & Awards
 Incorporating Graph-Based Models in a Deep Learning Framework for Operational Face Recognition
Note:
              This awardee has received supplemental funding. This award detail page includes information about both the original award and supplemental awards.
Award Information
Awardee
              Michigan State University
Award #
              2015-R2-CX-0005
Funding Category
              Continuation
Location
                    East Lansing, MI
Awardee County
              Ingham
Congressional District
              8
Status
              Closed
Funding First Awarded
              2015
Total funding (to date)
              $150,000
Original Solicitation
              NIJ FY 15 Graduate Research Fellowship Program in Science, Technology, Engineering, and Mathematics
Description of original award (Fiscal Year 2015, $50,000)
This award was competitively made in response to a proposal submitted by Michigan State University to a National Institute of Justice FY 2015 solicitation:  Graduate Research Fellowship in Science, Technology, Engineering & Mathematics.  With this solicitation, NIJ sought applications for funding innovative doctoral dissertation research in science, technology, engineering, or mathematics that is relevant to providing solutions to better ensure public safety, prevent and control crime, and ensure the fair and impartial administration of criminal justice in the United States.  The ultimate goal of this solicitation is to increase the pool of researchers in science, technology, engineering, and mathematics fields who are involved in research relevant to criminal justice applications. In its application, Michigan State University proposes to improve the process of automated face recognition by wrapping into the process demographic information such as known associates, gender and social networking data. This application proposes 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. It is proposed that 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.  This project is being funded incrementally in 3 phases, with the effort funded in FY15 representing the first phase.

This project contains a research and/or development component, as defined in applicable law.

ca/ncf
Supplemental Award 1 (Fiscal Year 2016, $50,000)
As submitted by the proposer:  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. In this work, 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. Experimental evaluation will be conducted on large datasets. ca/ncf
    Supplemental Award 2 (Fiscal Year 2017, $50,000)
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.
In this work,the researcher proposes 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.
This project contains a research and/or development component, as defined in the applicable law.
nca/ncf


  
Grant-Funded Publications
        
                      Label Propagation Approach for Predicting Missing Biographic Labels in Face-based Biometric records

  
Date Created: September 15, 2015
    
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