This paper presents a novel face alignment method for image sequences that integrates Gabor wavelets with the statistical AAM (Active Appearance Model). First, feature points are characterized using Gabor wavelets and can be individually tracked. A disadvantage of this kind of purely feature-based tracking is that errors accumulate and the feature points loose lock on their corresponding features. To overcome this problem, a face affine transform is used to obtain the initial shape of the AAM model. Finally, the AAM is used to impose global constraints upon the local feature points and to produce an exact alignment. Experimental results demonstrate the ability of the proposed algorithm to accurately align and locate facial features.
['Manhua Yu', 'Saiyi Li']
Face alignment based on statistical models and Gabor wavelets