基于多特征融合条件随机场的人脸图像分割

由于发型、头部姿势、服装、遮挡等现象的多样性，人脸图像分割一直是一个具有挑战性的课题。为了提高复杂背景图像的人脸分割正确性，提出了一种基于多特征融合条件随机场（CRFs）的方法。该模型建立在图模型上，图中的每一个节点对应一个超像素，每一条边缘则连接一对相邻的超像素。使用颜色和纹理特征定义节点的能量函数（一元能量函数），使用位置信息和相邻超像素之间的差异定义边缘的能量函数（二元能量函数）。分割是通过条件随机场融合节点能量函数和边缘能量函数推理而得。考察了该模型在2个无约束人脸数据库上的分割性能，实验结果表明该方法可以有效地从复杂人脸图像中分割出面部皮肤、头发和背景区域。

Face segmentation using CRFs based on multiple feature fusion

Face segmentation is quite challenging due to the diversity of hair styles, head poses, clothing, occlusions, and other phenomena. To improve the accuracy of face segmentation from the images with complex scenes, we present a method based on Conditional Random Fields (CRFs) in this paper. The CRFs model is defined on a graph, in which each node corresponds to a superpixel and each edge connects a pair of neighboring superpixels. The features of color and texture are used to define the node(unary) energy function, and the position distance and differences of features between adjacent superpixels are used to define the edge(binary) energy function. Segmentation is performed by inferring the CRFs model built by fusing node energy function and edge energy function. We evaluate the performance of the proposed method on two unconstrained face databases. Experimental results demonstrate that the proposed method can efficiently partition face.