A novel incremental principal component analysis and its application for face recognition
A novel incremental principal component analysis and its application for face recognition
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DOI：
10.1109/tsmcb.2006.870645
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发表时间：
2006-08
期刊：
影响因子：
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通讯作者：
Haitao Zhao;P. Yuen;J. Kwok
中科院分区：
文献类型：
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作者：
Haitao Zhao;P. Yuen;J. Kwok
主成分分析（PCA）已被证明是一种有效的模式识别和图像分析方法。近年来，PCA被广泛应用于人脸识别算法中，如特征脸、鱼脸等。令人鼓舞的结果已在文献中报道和讨论。在过去的十年里，许多基于pca的人脸识别系统也被开发出来。然而，现有的基于pca的人脸识别系统由于计算成本和内存需求负担而难以扩展。为了克服这一限制，通常采用增量方法。增量主成分分析（IPCA）方法在机器学习领域已经研究了很多年。现有IPCA方法的主要局限性是不能保证逼近误差。针对这一局限性，本文提出了一种新的基于奇异值分解（SVD）更新算法思想的IPCA方法，即基于SVD更新的IPCA （SVDU-IPCA）算法。在提出的SVDU-IPCA算法中，我们从数学上证明了近似误差是有界的。并对该方法进行了复杂度分析。提出的SVDU-IPCA算法的另一个特点是它可以很容易地扩展到内核版本。使用现有的公共数据库（即FERET， AR和Yale B）对所提出的方法进行了评估，并将其应用于现有的人脸识别算法。实验结果表明，增量方法与批处理方法的平均识别准确率差异小于1%。这意味着所提出的SVDU-IPCA方法与批模式PCA方法非常接近
Principal component analysis (PCA) has been proven to be an efficient method in pattern recognition and image analysis. Recently, PCA has been extensively employed for face-recognition algorithms, such as eigenface and fisherface. The encouraging results have been reported and discussed in the literature. Many PCA-based face-recognition systems have also been developed in the last decade. However, existing PCA-based face-recognition systems are hard to scale up because of the computational cost and memory-requirement burden. To overcome this limitation, an incremental approach is usually adopted. Incremental PCA (IPCA) methods have been studied for many years in the machine-learning community. The major limitation of existing IPCA methods is that there is no guarantee on the approximation error. In view of this limitation, this paper proposes a new IPCA method based on the idea of a singular value decomposition (SVD) updating algorithm, namely an SVD updating-based IPCA (SVDU-IPCA) algorithm. In the proposed SVDU-IPCA algorithm, we have mathematically proved that the approximation error is bounded. A complexity analysis on the proposed method is also presented. Another characteristic of the proposed SVDU-IPCA algorithm is that it can be easily extended to a kernel version. The proposed method has been evaluated using available public databases, namely FERET, AR, and Yale B, and applied to existing face-recognition algorithms. Experimental results show that the difference of the average recognition accuracy between the proposed incremental method and the batch-mode method is less than 1%. This implies that the proposed SVDU-IPCA method gives a close approximation to the batch-mode PCA method