This paper proposes a new multi-factor framework which unifies linear, bilinear, and quadratic models. The authors describe a new fitting algorithm which jointly estimates all model parameters and show that it outperforms the standard alternating algorithm.
With the emergence of new applications centered around the sharing of image data, questions concerning the protection of the privacy of people visible in the scene arise. Recently, formal methods for the de-identification of images have been proposed which would benefit from multi-factor coding to separate identity and non-identity related factors; however, existing multi-factor models require complete labels during training which are often not available in practice. With the emergence of new applications centered around the sharing of image data, questions concerning the protection of the privacy of people visible in the scene arise. Recently, formal methods for the de-identification of images have been proposed which would benefit from multi-factor coding to separate identity and non-identity related factors; however, existing multi-factor models require complete labels during training which are often not available in practice. In experiments on illumination-and expression-variant face datasets the authors show that the proposed algorithms achieve the desired privacy protection while minimally distorting the data. (Publisher abstract provided)