3D Face Fitting Method Based on 2D Active Appearance Models

Abstract

Special cameras such as 3D scanners or depth cameras are necessary in recognizing 3D shapes from input faces. In this paper, we propose an efficient face fitting method which is able to fit various faces including any variations of 3D poses (the rotation of X, Y axes) and facial expressions. Our method takes an advantage of 2D Active Appearance Models (AAM) from 2D face images rather than using the depth information measured by special cameras. We first construct an AAM for the variations of the facial expression. Then, we estimate depth information of each land-mark from frontal and side view images. By combining the estimated depth information with AAM, we can fit various 3D transformed faces. Self-occlusions due to the 3D pose variation are also processed by the region weighting function on the normalized face at each frame. Our experimental results show that the proposed method can efficiently fit various faces better than the typical AAM and View-based AAM.