Linear Disentangled Representation Learning for Facial Actions

Limited annotated data available for the recognition of facial expression and particularly action units makes it hard to train a deep network which can learn disentangled invariant features. However, a supervised linear model is undemanding in terms of training data. In this paper, we propose an elegant linear model to untangle facial actions from expressive face videos which contain a mixture of linearly-representable attributes. Previous attempts require an explicit decoupling of identity and expression which is practically inexact. Instead, we exploit the low-rank property across frames to implicitly subtract the intrinsic neutral face, which are modeled jointly with sparse representation only on the residual expression components. On CK+, our one-shot C-HiSLR on raw-face pixel-intensities performs far more competitive than conventional shape+SVM models with landmark detection and two-stepped SRC of the same type yet applied on manually prepared expression components. It is also comparable with the piecewise linear model DCS and temporal models, such as CRF and Bayes nets. We apply it to action unit (AU) recognition on MPI-VDB achieving a decent performance. As expression is a mixture of AUs, the result gives hopes of approximating an expression using a piecewise linear model.