Hidden Smile Correlation Discovery Across Subjects Using Random Walk with Restart

Abstract

Fine-grained smile analysis is a complicated and challenging process. Understanding other party’s smiles is one of the key tasks associated with realizing the implicit messages transmitted by the human. Considering this kind of message transmission is a major feature of human communication, understanding smiling has great potential value to promote the development of humanoid robots and animated software agents. Therefore, a fine-grained smile analysis system is proposed to uncover the hidden smile correlation across subjects. The system incorporates head pose as prior knowledge and employs conditional random forest to detect fiducial points on face. After that, a steady-state probability defined by a succession of Markov random steps is used to indicate the relevance score between smiles across subjects. We demonstrate performance of the proposed system on both constrained and unconstrained face datasets. The experimental results show that the proposed system is able to classify 4 smile levels and uncover hidden smile correlations across subjects successfully.