为了提高人脸识别率，本文提出了一种增量学习支持矢量机（SVM）人脸识别方法，有效地对SVM的参数进行更新。提出的方法采用高斯概率模型描述SVM的参数统计特征，在无需额外存储训练数据的前提下，采用增量学习SVM的方式实现参数的更新；并通过最小化分类误差准则最大化SVM两类输出值概率分布间的距离。详细的实验以及与现有方法的比较结果表明，提出的识别方法具有更好的识别性能。

To improve the face recognition rate, this paper proposes an incremental learning support vector machine (SVM) face recognition scheme to update the parameters of SVM efficiently. The proposed scheme adopts the Gaussian probability model to depict the parameters of SVM, and updates the parameters of SVM based on the incremental learning SVM without saving the training data. The proposed scheme also employs the rule that minimizes the error of classifications to maximize the distance of the output distributions of two classes. The detailed experimental results and comparisons with the existing schemes show that the proposed scheme can obtain better recognition performance.