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Automatic age estimation from facial images has attracted increasing attention due to its promising potential in real-life computer vision applications. However, due to uncontrollable environments, insufficient and incomplete training data, strong person-specific and large within-age span variations, age estimation has become a challenging problem. Among published age estimation, hierarchical age estimation methods have achieved comparable performance improvement than single level approaches. Most of the published hierarchical approaches have mainly used support vector machines to classify age groups followed by support vector regression for withina-age group age estimation. In this paper, we present a novel hierarchical Gaussian process framework for automatic age estimation. It consists of multi-class Gaussian process classifier to classify the input images into different age groups followed by a warped Gaussian process regression to model group specific aging patterns. In this paper, we separately tune the hyper-parameters for each age group at the regression stage. Compared with existing single level Gaussian process approaches for age estimation, our approach is computationally efficient at both the levels of hierarchy. Partitioning data into different age groups and learning group-wise hyper-parameters is computationally more efficient than learning complete training data. Misclassifications at the group boundaries are compensated at the regression stage by overlapping the neighboring age ranges. Finally, through extensive experiments on two popular aging datasets, the FG-NET and the Morph-II, we demonstrate the effectiveness of our algorithm in improving age estimation performance. INDEX TERMS Age estimation, Gaussian process regression, warped Gaussian process regression.

For decades of wind energy technology developments, much research on the subject has been carried out, and this has given rise to many works encompassing different topics related to it. As a logical consequence of such a research and editorial activity, state-of-the-art review works have also been published, reporting about a wide variety of research proposals. Review works are particularly interesting documents for researchers because they try to gather different research works on the same topic present their achievements to researchers. They act, in a way, as a guidance for researchers to quickly access the most meaningful works. The proposal of this paper consists of going one step further, and to present a review of state-of-the-art review works on wind-energy-related issues. A classification into several main topics in the field of energy research has been done, and review works that can be classified in all these areas have been searched, analyzed, and commented on throughout the paper.

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