基于局部和全局的特征提取算法及在人脸识别中的应用

Feature Extraction Algorithm Based on Locality and Globality and its Application in Face Recognition

提出了一种基于局部和全局特征的特征提取算法。该算法不仅能保持数据集的局部性,同时也考虑了数据集的全局性,使得降维后的数据既能保持邻近关系,又能从整体上较好地重构和展现。PCA()能较好地展现原数据集,LPP能保持局部邻近关系,算法结合了这两个算法的思想,但由于LPP没有考虑类别信息,故先对LPP进行改进,给出了一种有监督的局部保持投影算法,使得提出的算法能更加有利于分类问题。通过人脸识别实验,验证了算法的正确性和有效性。

A feature extraction algorithm based on locality and globality was proposed,which on one hand takes the locality of the data into consideration, on the other hand takes the globality of data into account. Consequently, the data after dimension reduction not only preserves the locality relationship, but also reconstructs and represents the original data perfectly. PCA （Principal Component Analysis） can represent the data nicely and LPP （Locality Preserving Projection） can preserve the locality relationship,so the algorithm just hybrid the two of them. But LPP does not utilize the classification information,so first LPP-based algorithm is improved,a supervised version is given,which results in that the algorithm is more suitable for classification task. Experiments in face recognition validate the correctness and effec- tiveness of the proposed algorithm.