基于QR分解的线性图嵌入算法与人脸识别

Linear Extension of Graph Embedding Based on QR Decomposition and Face Recognition

针对小样本问题,提出了一种基于QR分解的线性图嵌入(Linear Extension of Graph Embedding,LGE)求解算法,并将其用于人脸识别。与传统的用主成分分析进行降维不同,新算法利用QR分解对数据进行降维,然后在降维后的空间利用线性图嵌入算法进行二次特征抽取,最后利用最近邻分类器进行分类识别。新算法有效的解决了小样本问题,并且在降维的过程中不损失鉴别信息,提高了算法的识别率。在Yale和PIE人脸数据库的实验表明了本文算法在识别性能上优于传统算法。

In order to address small sample size （3S） problem, a new algorithm for implementing linear extension of graph embedding （LGE） based on QR decomposition is proposed, which could be used in face recognition. Different from the traditional approach of dimension reduction by Principle Component Analysis （PCA）, the new algorithm applies QR decomposition to implement dimension reduction. Then the LGE is followed and employed for the second feature extraction in the transformed space. Finally, the nearest neighbor classifier is used for classification and recognition. The new algorithm not only can effectively solve 3S problem, but also hold the discriminant information. Experimental results on YALE and PIE face databases show that the algorithm outperforms the traditional method in recognition rates.