Feature extraction on faces : from landmark localization to depth estimation

This thesis focuses on learning algorithms that extract important features from faces. The features of main interest are landmarks; the two dimensional (2D) or three dimensional (3D) locations of important facial features such as eye centers, nose tip, and mouth corners. Landmarks are used to solve complex tasks that cannot be solved directly or require guidance for enhanced performance, such as pose or gesture recognition, tracking, or face verification. The application of the models presented in this thesis is on facial images; however, the algorithms proposed are more general and can be applied to the landmarks of other forms of objects, such as hands, full body or man-made objects. This thesis is written by article and explores different techniques to solve various aspects of landmark localization. In the first article, we disentangle identity and expression of a given face to learn a prior distribution over the joint set of landmarks. This prior is then merged with a discriminative classifier that learns an independent probability distribution per landmark. The merged model is capable of explaining differences in expressions for the same identity representation. In the second article, we propose an architecture that aims at uncovering image features to do tasks that require high pixel-level accuracy, such as landmark localization or image segmentation. The proposed architecture gradually extracts coarser features in its encoding steps to get more global information over the image and then it expands the coarse features back to the image resolution by recombining the features of the encoding path. The model, termed Recombinator Networks, obtained state-of-the-art on several datasets, while also speeding up training. In the third article, we aim at improving landmark localization when only a few images with labelled landmarks are available. In particular, we leverage a weaker form of data labels that are easier to acquire or more abundantly available such as emotion or head pose. To do so, we propose an architecture to backpropagate gradients of the weaker labels through landmarks, effectively training the landmark localization network. We also propose an unsupervised loss component

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