Facial feature point extraction using the adaptive mean shape in active shape model

Authors:
Hyun-Chul Kim;Hyoung-Joon Kim;Wonjun Hwang;Seok-Cheol Kee;Whoi-Yul Kim
Affiliations:
Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea;Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea;Samsung Advanced Institute of Technology, Seoul, Gyeonggi-do, Korea;Samsung Advanced Institute of Technology, Seoul, Gyeonggi-do, Korea;Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea
Venue:
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Year:
2007

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Abstract

The fixed mean shape that is built from the statistical shape model produces an erroneous feature extraction result when ASM is applied to multipose faces. To remedy this problem the mean shape vector which is similar to an input face image is needed. In this paper, we propose the adaptive mean shape to extract facial features accurately for non frontal face. It indicates the mean shape vector that is the most similar to the face form of the input image. Our experimental results show that the proposed method obtains feature point positions with high accuracy and significantly improving the performance of facial feature extraction over and above that of the original ASM.