基于2DPCA和流形学习LPP算法的人脸特征提取应用

人脸图像的特征提取是人脸识别系统中最关键同时也是难题之一。流形学习算法是近些年的人脸识别和语音识别两个领域应用较多的非线性降维方法。通过对人脸识别系统的研究,现提出一种全新的基于2DPCA（Two-Dimentional PCA）和流形学习LPP（Locality Preserving Projections）算法的特征提取方法,可为今后深入研究人脸识别技术提供较好的参考。仿真实验表明,该算法与传统特征提取PCA、LDA算法相比,可以取得更好的识别率。

Face Feature Extraction Based on 2DPCA and LPP Manifold Learning Algorithm

Face-image feature extraction is one of the key technologies and problems in face recognition systems. Manifold learning algorithm, as a non-linear dimension reduction method, has been used in facial recognition field and speech recognition field in recent years. A new feature extraction based on 2DPCA（Two-Dimentional PCA） and LPP（Locality Preserving Projections） algorithm of the manifold learning is proposed through systematic study of facial recognition system. And it may provide a good reference for further study of facial recognition technology. The simulation experiment shows that this algorithm has better recognition rate as compared with PCA, LDA algorithms of traditional feature extraction.