Facial Expression Synthesis Using Manifold Learning and Belief Propagation

Abstract

Given a person's neutral face, we can predict his/her unseen expression by machine learning techniques for image processing. Different from the prior expression cloning or image analogy approaches, we try to hallucinate the person's plausible facial expression with the help of a large face expression database. In the first step, regularization network based nonlinear manifold learning is used to obtain a smooth estimation for unseen facial expression, which is better than the reconstruction results of PCA. In the second step, Markov network is adopted to learn the low-level local facial feature's relationship between the residual neutral and the expressional face image's patches in the training set, then belief propagation is employed to infer the expressional residual face image for that person. By integrating the two approaches, we obtain the final results. The experimental results show that the hallucinated facial expression is not only expressive but also close to the ground truth.

A brief survey on secure multi-party computing in the presence of rational parties

“…The above protocols based on game-theory models are useful for building incentives among distributed and distrustful parties. However, it cannot be directly applied to the following scenario, where some distrustful parties hope to complete the protocol (Huang and Su 2006;Alfredo and Ahmed 2011). What they care about is the strategies of the attackers, who have their own behavioral biases.…”