基于直接局部保持投影和尺度不变特征变换的人脸识别方法

摘要

The Scale Invariant Feature Transform ( SIFT) algorithm is robust to the feature extraction of face image. However, the feature data derived by SIFT is of high dimension, and is difficult to be handled. Therefore, a Direct Locality Preserving Projections-SIFT ( DLPP-SIFT) algorithm was proposed. In the algorithm, SIFT was used to extract feature, and the subspace method with Locality Preserving Projections ( LPP) was utilized for dimension reduction. This algorithm solved locality preserving problem via simultaneous diagonalization; therefore, the singularity of the matrix was avoided. The experiments on ORL and FERET face databases show that the proposed algorithm reduces the computation complexity and matching time of features successfully, and is more robust than SIFT, Principal Component Analysis (PCA) -SIFT and LPP-SIFT methods.%尺度不变特征变换(SIFT)算法提取的人脸特征具有一定的鲁棒性,但存在数据维数过高和计算过于复杂的问题.为此,提出一种基于直接局部保持投影-尺度不变特征变换(DLPP-SIFT)的人脸识别算法.首先采用SIFT算法进行特征提取,然后结合子空间方法局部保持投影(LPP)进行降维,利用直接对角化方法求取特征矩阵,解决了LPP的奇异值问题.在ORL和FERET人脸库的实验结果表明,DLPP-SIFT算法可显著减少计算复杂度和特征匹配时间,与SIFT、主成分分析(PCA)-SIFT、LPP-SIFT相比,具有更好的鲁棒性.