Abstract: The range of expression variety is discretized to be multi-state component model, which is used to describe nonlinear variety of a face. The hierarchical face alignment is achieved based on the cascade convolutional neural networks by introducing multi direction local gradient information and constructing the corresponding backprojection probability map to improve appearance representation of traditional grey images. The face shape initialization is realized and the current component state is predicted, according to the whole face and different regions. The face shape parameters are regressed to achieve final fine alignment, according to the chosen face model under correct state. The experiments performed on database demonstrate that the proposed method improves the robustness of face alignment under exaggerated expressions and correct rate, and convergence rate are increased under the considerable computational complexity.

Key words: backprojection probability map, multi-state component model, Convolutional Neural Network, shape parameters update, hierarchical face alignment

摘要： 将人脸表情变化范围离散化表示为多状态部件模型，以便描述人脸非线性变化。引入多方向局部梯度信息，建立反投影概率图来改善原始灰度图像的外观模式表达，基于级联的卷积神经网络实现渐进分层的人脸配准。根据整脸和不同区域的图像实现人脸形状初始化，并判断当前部件状态。根据正确状态的人脸模型回归人脸形状参数，完成最终的精细配准。与其他几种常用算法在数据库上进行了定量比较，结果表明该算法改善了表情变化剧烈时人脸配准的效果，在计算量相当的情况下，正确率和处理速度等方面都达到很好的性能，具有明显的实用价值。

关键词: 反投影概率图, 多状态部件模型, 卷积神经网络, 形状参数更新, 分层人脸配准