AAM-based feature point extraction for pose-variant face
Cui Rui · Computer Engineering and Applications Journal · 2010
Active Appearance Mode(lAAM) is the traditional classical method for feature point extraction.However,limited by the linear predicable model,it is very difficult to find the accurate feature points when the initial position is too far away from the destination,thus the single AAM template is hard to meet the requirement of pose-variant face.This paper proposes a face feature point extraction method based on multi-AAM templates:Firstly,template similarity and face feature points are defined and the face pose is classified;then,AAM template for each pose is trained;next,test images are searched with each template and similarity is calculated accordingly;through comparing the similarities,the best template with the highest similarity is chosen to detect the feature points.The experiments on Oriental Face Database prove the essentiality of template selection and the reasonability of similarity defined in this paper.The experimental results show that 92.11% of the whole test set can obtain the correct AAM template thus the feature points can be extracted accurately and 99.92% of test images can get the correct or the neighbor AAM template thus the feature points can be extracted.