基于局部线性嵌入与主成分分析的人脸识别方法

Face recognition based on locally linear embedding and principal component analysis

针对主成分分析(PCA)算法对数据进行向量化,破坏初始数据的局部结构信息的缺点,提出了将局部线性嵌入(LLE)与PCA相结合的人脸识别算法。先采用LLE提取的初始数据保留了人脸局部结构信息的低维特征,再利用PCA计算低维数据的主要成分,最后根据各人脸的主要成分之间的欧式距离判断是否匹配。对比实验表明,该算法在明显提升算法效率的同时,保证了较高的识别率。

Focusing on the disadvantage that the principal component analysis （PCA） algorithm destroy the primary data of local structural information when it vectors the data, a face recognition algorithm that combines the locally linear embedding （LLE） with PCA was proposed. First, the low-dimensional features from the initial data which preserving the local structure information of face image was extracted by LLE. Secondly, the main components of the low-dimensional data with PCA were calculated. At last, the main components were judged whether they are matching or not according to their Euclidean distance. Comparative experimental result shows that this algorithm keeps a high recognition rate when improving the arithmetic efficiency.