Face-deidentification in images using Restricted Boltzmann Machines

摘要

In this work, we discuss utility of Restricted Boltzmann Machine (RBM) in face-deidentification challenge. GRBM is a generative modeling technique and its unsupervised learning provides vantage of using raw faces data. Faces are deidentified by reconstructed face images from the trained GRBM model. The reconstructed image uses random information from the stochastic units which makes it hard to re-identify from the deidentified face. Experiments show the proposed technique maintain emotions in the test face, which is intrinsic to the modeling capacity of RBM.