Pose Classification of Human Faces Using Mask Functions

摘要

We introduce an approach for automatic estimation of the poses/degrees of human faces embedded in complicated circumstances. The proposed system consists of two primary parts. The first part is to search the potential face regions that are gotten from the isosceles-triangle-based on the rules of "the combination of two eyes and one mouth ". In the first part, we read in an image first, and then convert the input image to a binary image. Secondly, label all 4-connected components and detect any 3 centers of 3 different blocks that form an isosceles triangle. Then, clip the regions that satisfy the isosceles triangle criteria as the potential face regions. The second part of the proposed system is to perform pose verification. In the second part, each face candidate obtained from the previous process is normalized to a standard size (60*60 pixels). Then, each of these normalized potential face regions is fed to the face weighting mask function to get the location of face region. Next, fed to the direction weighting mask function to judge which direction the matching face region looking at. Last, fed to the pose weighting mask function to decide the poses/degrees of the human faces.