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 kn different 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.
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机译：公开了一种统计面部特征提取方法。在训练阶段，将N个训练面部图像分别标记为位于n个不同块中的n个特征点，以形成N个特征向量。接下来，在将每个形状矢量与参考形状矢量对齐之后，使用主成分分析（PCA）技术获得统计的面部形状模型。在执行阶段，首先根据在训练阶段获得的对齐后的训练面部图像的平均形状的坐标，猜测出所需面部特征的初始位置，在对应于上述初始位置的n个搜索范围内分别标记k个候选以获得k n 个测试形状向量的不同组合。最后，将与用于对准的训练面部图像的平均形状和统计面部形状模型具有最佳相似性的测试形状矢量的坐标分配为测试面部图像的面部特征。
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