Active shape models—their training and application
Computer Vision and Image Understanding
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Active Appearance Models Revisited
International Journal of Computer Vision
A model based method for automatic facial expression recognition
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Since the goal of Active Appearance Model (AAM) is to minimize the residual error between the model appearance and the input image, it often fails to converge accurately to the landmark points of the input image. To alleviate this weakness, we have combined Active Shape Model (ASM) into AAM, where ASM tries to find correct landmark points using the local profile model. Because the original objective function and search scheme of the ASM is not appropriate for combining these methods, we modified the objective function of the ASM and proposed a new objective function that combining that of two methods. The proposed objective function can be optimized using a gradient based algorithm as in the AAM. Experimental results show that the proposed method reduces the average fitting error when compared with existing fitting methods such as ASM, AAM, and Texture Constrained-ASM (TC-ASM).