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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2011
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| Online Access: | https://www.redalyc.org/articulo.oa?id=61520767005 |
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Table of Contents:
- Class-Conditional Probabilistic Principal Component Analysis: Application to Gender Recognition Juan Bekios Calfa José M. Buenaposada Luis Baumela Computación face analysis Gender classification class conditional PPCA This paper presents a solution to the problem of recognizing the gender of a human face from an image. We adopt a holistic approach by using the cropped and normalized texture of the face as input to a Naíve Bayes classifier. First it is introduced the Class-Conditional Probabilistic Principal Component Analysis (CC-PPCA) technique to reduce the dimensionality of the classification attribute vector and enforce the independence assumption of the classifier. This new approach has the desirable property of a simple parametric model for the marginals. Moreover this model can be estimated with very few data. In the experiments conducted we show that using CC-PPCA we get 90% classification accuracy, which is similar result to the best in the literature. The proposed method is very simple to train and implement. 2011 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61520767005 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.4 Vol.14