Recovering joint and individual components in facial data

A set of images depicting faces with different expressions or in various ages consists of components that are shared across all images (i.e., joint components) and imparts to the depicted object the properties of human faces and individual components that are related to different expressions or age groups. Discovering the common (joint) and individual components in facial images is crucial for applications such as facial expression transfer. The problem is rather challenging when dealing with images captured in unconstrained conditions and thus are possibly contaminated by sparse non-Gaussian errors of large magnitude (i.e., sparse gross errors) and contain missing data. In this paper, we investigate the use of a method recently introduced in statistics, the so-called Joint and Individual Variance Explained (JIVE) method, for the robust recovery of joint and individual components in visual facial data consisting of an arbitrary number of views. Since, the JIVE is not robust to sparse gross errors, we propose alternatives, which are 1) robust to sparse gross, non-Gaussian noise, 2) able to automatically find the individual components rank, and 3) can handle missing data. We demonstrate the effectiveness of the proposed methods to several computer vision applications, namely facial expression synthesis and 2D and 3D face age progression in-the-wild.

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