基于状态估计的张量分解人脸识别方法

张量脸算法是分析和表达多因素影响的人脸图像结构的一种有效的数学模型,然而张量分解对状态空间的非线性处理仍存在不足之处。对此提出了一种新的多姿态人脸图像识别方法,在原有的张量脸算法基础上结合状态估计的方法。将训练样本图库中不同状态的人脸通过PCA分解得到多种状态(角度、光照、表情)分别对应的特征空间,对于测试样本先投影到每个特征空间,利用最近邻分类器进行状态估计,对利用张量脸算法得到的张量脸进行识别。实验结果表明,该特征提取方法的识别率优于原有的张量脸算法。

Face recognition method based on state estimation and tensorfaces algorithm

Tensorfaces algorithm is an effective mathematical model which can analyze and express the frames of multi-view face images,but there are some problems of multi-linear analysis method with nonlinear changes of face images.So an improved tensorfaces algorithm is proposed for multi-view face recognition which integrates state estimation.The train-ing face images from different states are decomposed to some eigenspaces（views,illuminations and expressions） by PCA.Then the testing face images can be projected into each eigenspace and estimate the states of the unknown images by the closest classifier.It can recognize the faces by tensorface of every image which is obtained by the tensorfaces algorithm.Ex-perimental results show that this method outperforms the original tensorfaces method.