Abstract: Automatic face classification is currently a popular research area in Computer Vision. It involves several subproblems, such as subject recognition, gender classification or subject verification.
Current systems of automatic face classification need a large amount of training data to robustly learn a task. However, the collection of labeled data is usually a difficult issue. For this reason, the research on methods that are able to learn from a small sized training set is essential.
The dependency on the abundance of training data is not so evident in human learning processes. We are able to learn from a very small number of examples, given that we use, additionally, some prior knowledge to learn a new task. For example, we frequently find patterns and analogies from other domains to reuse them in new situations, or exploit training data from other experiences.
In computer science, Multitask Learning is a new Machine Learning approach that studies this idea of knowledge transfer among different tasks, to overcome the effects of the small sample sized problem.
This thesis explores, proposes and tests some Multitask Learning methods specially developed for face classification purposes. Moreover, it presents two more contributions dealing with the small sample sized problem, out of the Multitask Learning context. The first one is a method to extract external face features, to be used as an additional information source in automatic face classification problems. The second one is an empirical study on the most suitable face image resolution to perform automatic subject recognition.