Statistical facial feature extraction method

Details Statistical facial feature extraction method Statistical facial feature extraction method

2008-05-13

2008-05-13

Bella, Matthew C. (Department: 2624)

Image analysis

Applications

Personnel identification

Reexamination Certificate

active

07372981

ABSTRACT:
A statistical facial feature extraction method is disclosed. In a training phase, N training face images are respectively labeled n feature points located in n different blocks to form N feature vectors. Next, a principal component analysis (PCA) technique is used to obtain a statistical face shape model after aligning each shape vector with a reference shape vector. In an executing phase, initial positions for desired facial features are firstly guessed according to the coordinates of the mean shape for aligned training face images obtained in the training phase, and k candidates are respectively labeled in n search ranges corresponding to above-mentioned initial positions to obtain kndifferent combinations of test shape vectors. Finally, coordinates of the test shape vector having the best similarity with the mean shape for aligned training face image and the statistical face shape model are assigned as facial features of the test face image.

REFERENCES:
patent: 5164992 (1992-11-01), Turk et al.
A. Lanitis et. al.,Automatic face identification system using flexible appearance models, Image and Vision Computing, vol. 13, No. 5, Jun. 1995, pp. 393-401.
T. F. Cootes et. al.,Active Shape Models—Their Training and Application, Computer Vision And Image Understanding, vol. 61, No. 1, Jan. 1995, pp. 38-59, England.
Ming-Huan Yang et. al.,Detecting Faces in Images: A Survey, IEEE Transactions On Pattern Analysis And Machien Intelligence, vol. 24, No. 1, Jan. 2002, pp. 34-58.

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