A two-stage approach to automatic face alignment

Tong Wang; Haizhou Ai; Gaofeng Huang

doi:10.1117/12.539038

25 September 2003 A two-stage approach to automatic face alignment

Tong Wang, Haizhou Ai, Gaofeng Huang

Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003) https://doi.org/10.1117/12.539038
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China

Abstract

Face alignment is very important in face recognition, modeling and synthesis. Many approaches have been developed for this purpose, such as ASM, AAM, DAM and TC-ASM. After a brief review of all those methods, it is pointed out that these approaches all require a manual initialization to the positions of the landmarks and are very sensitive to it, and despite of all those devoted works the outline of a human face remains a difficult task to be localized precisely. In this paper, a two-stage method to achieve frontal face alignment fully automatically is introduced. The first stage is landmarks' initialization called coarse face alignment. In this stage, after a face is detected by an Adaboost cascade face detector, we use Simple Direct Appearance Model (SDAM) to locate a few key points of human face from the texture according which all the initial landmarks are setup as the coarse alignment. The second stage is fine face alignment that uses a variant of AAM method in which shape variation is predicted from texture reconstruction error together with an embedded ASM refinement for the outline landmarks of the face to achieve the fine alignment. Experiments on a face database of 500 people show that this method is very effective for practical applications.

Citation Download Citation

Tong Wang, Haizhou Ai, and Gaofeng Huang "A two-stage approach to automatic face alignment", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); https://doi.org/10.1117/12.539038

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