A new facial feature extraction method based on linear combination model

Yongli Hu, Baocai Yin, Dehui Kong
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引用次数: 10

Abstract

A new facial feature extraction method is proposed. Based on linear combination model, the method locates feature points in facial images precisely. The model uses the knowledge of prototypic faces to interpret novel faces. To get the knowledge, the prototypes are labeled manually on the feature points. Generally, the construction of the linear combination model depends on pixel-wise alignments of prototypes, and the alignments are computed by an optical flow algorithm or bootstrapping algorithm which is a full-scale optimization and not includes local information such as facial feature points. To combine local facial feature with the linear combination model, a restrained optical flow algorithm is proposed to compute the pixel-wise alignments. With the information of labeled feature points, the model matches the input facial images and extracts the feature points automatically. Implementing the feature extraction method on the MPI face database, the experimental results show that the method has good performance.
一种基于线性组合模型的人脸特征提取新方法
提出了一种新的人脸特征提取方法。该方法基于线性组合模型,对人脸图像中的特征点进行精确定位。该模型利用原型面孔的知识来解释新面孔。为了获得知识,原型在特征点上被手工标记。一般来说,线性组合模型的构建依赖于原型的逐像素对齐,并且对齐是通过光流算法或自举算法进行计算的,这是一种全尺度优化算法,不包含面部特征点等局部信息。为了将局部人脸特征与线性组合模型相结合,提出了一种约束光流算法来计算逐像素对齐。利用标记的特征点信息,对输入的人脸图像进行匹配,自动提取特征点。在MPI人脸数据库上实现了特征提取方法,实验结果表明该方法具有良好的性能。
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