Patent ID: 7372981

Claim:
A statistical facial feature extraction method, comprising: a first procedure for creating a statistical face shape model based on a plurality of training face images, including: an image selecting step, to select N training face images; a feature labeling step, to respectively label feature points located in n different blocks of the training face images to define corresponding shape vectors of the training face images; an aligning step, to align each shape vector with a reference shape vector to thus obtain aligned shape vectors; and a statistical face shape model computing step, to use a principal component analysis (PCA) process to compute a plurality of principal components based on the aligned shape vectors to create a statistical face shape model, wherein the statistical face shape model represents the shape vectors by combining a plurality of projection coefficients, and the statistical face shape model computing step includes: computing a mean value of the feature points of the aligned shape vectors to define a mean shape vector x as x _ = 1 N ⁢ ∑ a = 1 N ⁢ ⁢ x a where x a is an aligned shape vector, subtracting each aligned shape vector x a by the mean shape vector x to form a matrix A=└d x1 ,d x2 , . . . ,d xN ┘ and d x a =x a − x , computing a covariance matrix C of the matrix A, and computing a plurality of principal components according to eigenvectors v k s , which are derived from Cv k s =λ k s v k s with eigenvalues λ k s corresponding to the covariance matrix C formed as C=AA T for 1≦k≦m where m is the dimension of the covariance matrix C for λ 1 s ≧λ 2 s ≧ . . . ≧λ m s , to thereby form the statistical face shape model; and a second procedure for extracting a plurality of facial features from a test face image, including: an initial guessing step, to guess initial positions of n test feature points located in the test face image, wherein the initial position of each test feature point is a mean value of the feature points of the aligned shape vectors; a search range defining step, to define n search ranges in the test face image, based on the initial position of each test feature point, wherein each search range corresponds to a different block; a candidate feature point labeling step, to label a plurality of candidate feature points for each search range; a test shape vector forming step, to do combination of the candidate feature points in different search ranges in order to form a plurality of test shape vectors; and a determining step, to match the test shape vectors respectively to both the mean value and the principal components for computing a similarity, and to accordingly assign one feature point corresponding to one, having the best similarity, of the test shape vectors as facial features of the test face image.