Active conditional models

— Matching images with large geometric and iconic changes (e.g. faces under different poses and facial expressions) is an open research problem in computer vision. There are two fundamental approaches to solve the correspondence problem in images: Feature-based matching and model-based matching. Feature-based matching relies on the assumption that features are stable across view-points and iconic changes, and it uses some unary, pair-wise or higher-order constraints as a measure of correspondence. On the other hand, model-based approaches such as Active Shape Models (ASMs) align appearance features with respect to a model. The model is learned from handlabeled samples. However, model-based approaches typically suffer from lack of generalization to untrained situations. This paper proposes Active Conditional Models (ACM) that combines the benefits of both approaches. ACM learns the conditional relation (both in shape and appearance) between a reference view of the object and other vi...

• Type: Journal
• Year: 2011
• Where: FGR
• Authors: Ying Chen, Fernando De la Torre
• Keywords: Acm Model, Biometrics, Correspondence Problem, FGR 2011, Shape Models