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    Automatic age and gender classification using supervised appearance model

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    Publication date
    2016-08-01
    Author
    Bukar, Ali M.
    Ugail, Hassan
    Connah, David
    Keyword
    Supervised appearance model; Age estimation; Gender classification; Partial least-squares regression; Regression
    Rights
    © 2016 The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
    Peer-Reviewed
    Yes
    
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    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.
    URI
    http://hdl.handle.net/10454/8760
    Version
    published version paper
    Citation
    Bukar AM, Ugail H and Connah D (2016) Automatic age and gender classification using supervised appearance model. Journal of Electronic Imaging. 25(6): 061605.
    Link to publisher’s version
    http://dx.doi.org/10.1117/1.JEI.25.6.061605
    Type
    Article
    Collections
    Engineering and Informatics Publications

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