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We present techniques for improving performance driven facial animation,emotion recognition, and facial key-point or landmark prediction using learnedidentity invariant representations. Established approaches to these problemscan work well if sufficient examples and labels for a particular identity areavailable and factors of variation are highly controlled. However, labeledexamples of facial expressions, emotions and key-points for new individuals aredifficult and costly to obtain. In this paper we improve the ability oftechniques to generalize to new and unseen individuals by explicitly modelingpreviously seen variations related to identity and expression. We use aweakly-supervised approach in which identity labels are used to learn thedifferent factors of variation linked to identity separately from factorsrelated to expression. We show how probabilistic modeling of these sources ofvariation allows one to learn identity-invariant representations forexpressions which can then be used to identity-normalize various procedures forfacial expression analysis and animation control. We also show how to extendthe widely used techniques of active appearance models and constrained localmodels through replacing the underlying point distribution models which aretypically constructed using principal component analysis withidentity-expression factorized representations. We present a wide variety ofexperiments in which we consistently improve performance on emotionrecognition, markerless performance-driven facial animation and facialkey-point tracking.

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