Learning to combine local models for Facial Action Unit detection

Abstract-Current approaches to automatic analysis of facial action units (AU) can differ in the way the face appearance is represented. Some works represent the whole face, dividing the bounding box region in a regular grid, and applying a feature descriptor to each subpatch. Alternatively, it is also common to consider local patches around the facial landmarks, and apply appearance descriptors to each of them. Almost invariably, all the features from each of these patches are combined into a single feature vector, which is the input to the learning routine and to inference. This constitutes the socalled feature-level fusion strategy. However, it has recently been suggested that decision-level fusion might provide better results. This strategy trains a different classifier per region, and then combines prediction scores linearly. In this work we extend this idea to model-level fusion, employing Artificial Neural Networks with an equivalent architecture. The resulting method has the advantage of learning the weights of the linear combination in a data-driven manner, and of jointly learning all the regionspecific classifiers as well as the region-fusion weights. We show in an experiment that this architecture improves over two baselines, representing typical feature-level fusion. Furthermore, we compare our method with the previously proposed linear decision-level region-fusion method, on the challenging GEMEP-FERA database, showing superior performance

Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

We present techniques for improving performance driven facial animation, emotion recognition, and facial key-point or landmark prediction using learned identity invariant representations. Established approaches to these problems can work well if sufficient examples and labels for a particular identity are available and factors of variation are highly controlled. However, labeled examples of facial expressions, emotions and key-points for new individuals are difficult and costly to obtain. In this paper we improve the ability of techniques to generalize to new and unseen individuals by explicitly modeling previously seen variations related to identity and expression. We use a weakly-supervised approach in which identity labels are used to learn the different factors of variation linked to identity separately from factors related to expression. We show how probabilistic modeling of these sources of variation allows one to learn identity-invariant representations for expressions which can then be used to identity-normalize various procedures for facial expression analysis and animation control. We also show how to extend the widely used techniques of active appearance models and constrained local models through replacing the underlying point distribution models which are typically constructed using principal component analysis with identity-expression factorized representations. We present a wide variety of experiments in which we consistently improve performance on emotion recognition, markerless performance-driven facial animation and facial key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS