Principal component analysis (PCA) has been successfully employed for face recognition. However, if the training process occurs frequently, owing to the update or downdate of the face images used for training, batch PCA becomes prohibitively expensive to recalculate. To overcome this limitation, incremental principal component analysis (IPCA) and decremental principal component analysis (DPCA) can be utilized as a good alternative to PCA because it reuses their previous results for its updates. Many IPCA or DPCA algorithms have been proposed; however, inaccurate tracking of the mean values of the face image data accumulates decomposition errors, which results in poor performance compared with batch PCA. We proposed faster and more accurate algorithms for IPCA and DPCA that maintain accurate decomposition results. The experimental results reveal that the proposed algorithms produce eigenvectors that are significantly close to the eigenvectors of batch PCA and exhibit faster execution speed for face recognition.

中文翻译：

---

用于人脸识别在线学习的快速更准确的增量-减量主成分分析算法

主成分分析 (PCA) 已成功用于人脸识别。然而，如果训练过程频繁发生，由于用于训练的人脸图像的更新或更新，批量 PCA 的重新计算变得非常昂贵。为了克服这个限制，增量主成分分析 (IPCA) 和递减主成分分析 (DPCA) 可以作为 PCA 的一个很好的替代方案，因为它会重复使用它们以前的结果进行更新。已经提出了很多 IPCA 或 DPCA 算法；然而，对人脸图像数据平均值的不准确跟踪会累积分解误差，导致与批量 PCA 相比性能较差。我们为 IPCA 和 DPCA 提出了更快、更准确的算法，以保持准确的分解结果。