Yes / 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/8760 |
| Date | 01 August 2016 |
| Creators | Bukar, Ali M., Ugail, Hassan, Connah, David |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, published version paper |
| 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. |
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