基于几何图像滤波的3D人脸识别算法  

3D face recognition by applying filter based on geometry image

作　　者：蔡亮[1] 达飞鹏[1] 

机构地区：[1]东南大学自动化学院,南京210096

出　　处：《东南大学学报（自然科学版）》2012年第5期859-863,共5页Journal of Southeast University：Natural Science Edition

基　　金：国家自然科学基金资助项目(51175081);江苏省自然科学基金资助项目(BK2010058)

摘　　要：针对表情变化下的三维人脸识别问题,提出了一种基于几何图像滤波的特征提取方法,并根据样本图像滤波后的特征分布函数给出最优卷积滤波器的设计过程.首先,利用网格平面参数化方法,将人脸网格映射到边界为正四边形的平面区域内,经过线性插值采样得到具有三维形状的二维几何图像;然后,将整体几何图像切割成局部分块图像的集合,在每组局部分块图像构成的训练样本库中利用差分进化算法对滤波器进行优化设计;最后,利用训练得到的最优滤波器提取对应分块图像的局部特征并计算相似度,将相似度得分融合,即可得到最终识别结果.利用FRGC v2人脸数据库进行实验验证,结果表明,使用几何图像滤波能显著提高算法的精度和鲁棒性.Aiming at the 3D face recognition under expression variation, a feature extraction method by applying filter on geometry image is proposed. The design for the optimal convolution filter is presented based on the distribution function of filtered features. First, after objectively mapping facial mesh into square domain based on mesh parameterization, a 2D geometry image with 3D shape is obtained by linear interpolation. Then, the entire images in the training set are segmented into pat- ches which are used for the differential evolution algorithm to design the optimal convolution filters. Finally, the similarity scores between local features are computed by applying these filters on corre- sponding patches, and the final decision is made by combining results of these scores. The experi- mental results from FRGC （face recognition grand challenge） v2 （version 2 ） databases show that both accuracy and robustness are improved by applying filter on geometry image.

关 键 词：3D人脸识别 形状滤波 几何图像 卷积滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]