Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

Authors:
George Toderici;Sean M. O'Malley;George Passalis;Theoharis Theoharis;Ioannis A. Kakadiaris
Affiliations:
Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, USA 77204-3010;Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, USA 77204-3010;Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, USA 77204-3010 and Computer Graphics Laboratory, Department of Informatics & Telecommunications, Univ ...;Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, USA 77204-3010 and Computer Graphics Laboratory, Department of Informatics & Telecommunications, Univ ...;Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, USA 77204-3010
Venue:
International Journal of Computer Vision
Year:
2010

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Abstract

While the retrieval of datasets from human subjects based on demographic characteristics such as gender or race is an ability with wide-ranging application, it remains poorly-studied. In contrast, a large body of work exists in the field of biometrics which has a different goal: the recognition of human subjects. Due to this disparity of interest, existing methods for retrieval based on demographic attributes tend to lag behind the more well-studied algorithms designed purely for face matching. The question this raises is whether a face recognition system could be leveraged to solve these other problems and, if so, how effective it could be. In the current work, we explore the limits of such a system for gender and ethnicity identification given (1) a ground truth of demographically-labeled, textureless 3-D models of human faces and (2) a state-of-the-art face-recognition algorithm. Once trained, our system is capable of classifying the gender and ethnicity of any such model of interest. Experiments are conducted on 4007 facial meshes from the benchmark Face Recognition Grand Challenge v2 dataset.