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

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 h...

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Bibliographic Details
Published in:International journal of computer vision Vol. 89; no. 2-3; pp. 382 - 391
Main Authors: Toderici, George, O’Malley, Sean M., Passalis, George, Theoharis, Theoharis, Kakadiaris, Ioannis A.
Format: Journal Article
Language:English
Published: Boston Springer US 01.09.2010
Springer
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Biometry
Classification
Computer Imaging
Computer Science
Demographics
Demography
Ethnicity
Face recognition
Gender
Ground truth
Human
Human subjects
Image Processing and Computer Vision
Mathematical models
Methods
Pattern Recognition
Pattern Recognition and Graphics
Retrieval
Studies
Vision
Retrieval
Ethnicity
Race
Identification
Gender
Face
Recognition
ISSN:0920-5691, 1573-1405
Online Access:Get full text
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Summary: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.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-009-0300-7