Multi-View Active Shape Model with Robust Parameter Estimation

Active shape model is an efficient way for localizing objects with variable shapes. When ASM is extended to multi-view cases, the parameter estimation approaches in previous works are often sensitive to the initial view, as they do not handle the unreliability of local texture search, which can be caused by bad initialization or cluttered background. To overcome this problem, we propose a novel algorithm for parameter estimation, using robust estimators to remove outliers. By weighting dynamically, our method acts as a model selection method, which reveals the hidden shape and view parameters from noisy observations of local texture models. Experiments and comparisons on multi-view face alignment are carried out to show the efficiency of our approach