主动外观模型是基于统计分析建立物体2维模型的有效方法，它融合了目标的形状和纹理信息。在基于相关型图像传感器3维人脸成像的基础上，提出了一种建立3维人脸模型的方法，该方法利用由相关型图像传感器得到的深度信息和与之对应的亮度信息将2维AAMs扩展为3维AAMs，融合人脸的形状，纹理和深度信息来构建3维人脸模型。人脸识别实验结果表明，该方法在不同人脸姿态，表情和光照条件下识别效果要优于Eigenface和2维AAMs。

Active Appearance Models (AAMs) is an effective statistical method to build 2D model for an object, which combines shape and texture information.A novel method for building 3D face model is proposed, which makes use of the depth information and corresponding intensity information generated by correlation image sensor (CIS), and extends 2D AAMs to 3D AAMs. The proposed improved AAMs fuse the shape, texture and depth information of face to build 3D face model. In facial recognition experiments which using 3D facial images based on CIS imaging system, the improved 3D AAMs model shows better recognition result than traditional AAMs algorithms and Eigenface.