Video-based face model fitting using Adaptive Active Appearance Model

摘要

Active Appearance Model (AAM) represents the shape and appearance of an object via two low-dimensional subspaces, one for shape and one for appearance. AAM for facial images is currently receiving considerable attention from the computer vision community. However, most existing work focuses on fitting an AAM to a single image. For many applications, effectively fitting an AAM to video sequences is of critical importance and challenging, especially considering the varying quality of real-world video content. This paper proposes an Adaptive Active Appearance Model (AAAM) to address this problem, where both a generic AAM component and a subject-specific appearance model component are employed simultaneously in the proposed fitting scheme. While the generic AAM component is held fixed, the subject-specific model component is updated during the fitting process by selecting the frames that can be best explained by the generic model. Experimental results from both indoor and outdoor representative video sequences demonstrate the faster fitting convergence and improved fitting accuracy.