基于多子空间直和特征融合的人脸识别算法

Face Recognition Algorithm of Feature Fusion Based on Multi-Subspaces Direct Sum

多个子空间直和能保证多个子空间数据融合时多个子空间得到的特征向量相互两两正交,融合数据采用该特征表示时冗余最小,更有利于分类识别。本文基于多子空间直和进行特征融合,提出了一种新的人脸识别算法。通过2DPCA算法,首先分别计算所有训练样本归一化后正脸、左侧脸及右侧脸图像的协方差矩阵的各P个最大特征值对应的P个相互正交的特征向量,然后通过选取3个子空间的部分满足直和条件的特征向量组成各自的特征空间(投影空间),再将样本正脸、左侧脸及右侧脸图像分别向各自特征空间投影得到3个特征矩阵,最后将此3个特征矩阵融合为该样本的特征矩阵用于最近邻分类器进行分类识别。最终通过本文3组实验数据的对比说明了该算法能减少计算量并且提高了识别率。

Redundancy of the multi-subspaces＇ fusion data represent by features can be minimized with the direct summation over multi-subspaces. In this paper, a new face recognition method based on feature fu- sion was proposed via using the direct summation of multi-subspaces. First we calculate the covariance matrices of all training samples＇front face, left face and right face images, which are all normalized, and then calculate their first P largest eigenvalues and corresponding mutually orthogonal eigenvectors, using the 2DPCA algorithm. Then we constitute three feature space （projection space） via three multi--subspaces＇ orthogonal eigenveetors which meet the direct sum condition. The samples＇ front face, left face and right face images are projected into the three spaces respectively. The projected features are fused as the classification feature. The comparison on the three groups of experimental results shows that our algorithm not only reduce the computation hut also increase the recognition rate.