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Journal of Electronic Imaging • Open Access

Automatic age and gender classification using supervised appearance model
Author(s): Ali Maina Bukar; Hassan Ugail; David Connah

Paper Abstract

Age and gender classification are two important problems that recently gained popularity in the research community, due to their wide range of applications. Research has shown that both age and gender information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical model that captures shape and texture variations, has been one of the most widely used feature extraction techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when used for classification. This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the problems of age and gender classification. Our experiments show that sAM has better predictive power than the conventional AAM.

Paper Details

Date Published: 1 August 2016
PDF: 11 pages
J. Electron. Imag. 25(6) 061605 doi: 10.1117/1.JEI.25.6.061605
Published in: Journal of Electronic Imaging Volume 25, Issue 6
Show Author Affiliations
Ali Maina Bukar, Univ. of Bradford (United Kingdom)
Hassan Ugail, Univ. of Bradford (United Kingdom)
David Connah, Univ. of Bradford (United Kingdom)

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