基于纹理分布和变形模板的面部特征提取

Facial Feature Extraction Based on Facial Texture Distribution and Deformable Template

面部特征提取是面部感知的重要内容 ,同时也是特定人的 3D人脸动画应用中所必须的前期工作 .在一个多级人脸检测模块检测到人脸大致区域和尺寸的基础上 ,提出并实现了一种基于面部图像纹理分布特性和可变形模板的由粗到细的面部特征提取策略 ,旨在解决可变形模板对参数初值依赖性强和计算时间长的问题 .该策略首先利用眼睛区域的谷特性和频率特性定位两个虹膜中心点位置 ,然后用积分投影确定唇部和鼻子区域的位置 ,在此基础上进行关键特征点的检测 ,从而可以得到预定义特征模板参数的良好初值 ,最后基于贪心算法的多阶段轮换优化算法来搜索一个极小点 .实验表明 ,该方案在时间和精度方面具有良好的性能 .

Facial feature extraction is an important aspect in facial image perception system. And it is also a prerequisite in animation system for generating a given person's 3D face image. In this paper, a coarse to fine facial feature extraction strategy is presented based on facial texture distribution and deformable template, using the pre result of a multi level face detection, which aims at solving such problems as the searching highly depending on the initial parameters and time consuming that deformable template algorithm often suffers from. In proposed strategy, firstly, the center of the two irises is localized making use of the valley and frequency characteristics in the two eye regions. Then integral projection is used to localize the coarse position of the mouth and the nose. Secondly, some key feature points about these organs are estimated. Finally, according to these feature points, good initial parameters for the pre defined templates are given and an optimal algorithm based on greedy algorithm and multi epoch cycle is used to search for the minimum solution. Experiments indicate that the implementation of the proposed strategy is with good performance in both speed and accuracy.