Abstract: Principal Component Analysis （PCA） has been proven to be an efficient method in pattern recognition.Recently，PCA has been extensively employed for face recognition algorithms，such as eigenface and fisherface.But PCA-based face-recognition systems are hard to scale up in applying because of incremental learning.This paper proposes a new Incremental Principal Component Analysis for Batch-Incremental data.First apply the space projection transformation based on the original PCs，then compute the unitary PCA in a lower-dimension transformed space.So the computation complexity can be reduced.And propose the kernel form of the incremental method at the same time.The experimental results on ORL face database verify that the presented approach is efficient.

摘要： 主成分分析（PCA）是模式识别领域一种重要的方法，现在已被广泛地应用于人脸识别算法中，但基于PCA人脸识别系统在应用中面临着一个重要障碍：增量学习问题。针对这个问题，提出了一种适用于成批增量数据的IPCA算法，该算法在原始PCA分解的基础上，利用空间投影变换，使得可以在一个低维空间求解整体PCA，从而降低了求解的复杂度，在此基础上对该增量算法进行了核化，并在ORL人脸数据库上验证了算法的有效性。