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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Radial Basis Function Neural Network With Incremental Learning for Face Recognition
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Radial Basis Function Neural Network With Incremental Learning for Face Recognition

机译：径向基函数神经网络的增量学习的人脸识别

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摘要

Conventional face recognition suffers from problems such as extending the classifier for newly added people and learning updated information about the existing people. The way to address these problems is to retrain the system which will require expensive computational complexity. In this paper, a radial basis function (RBF) neural network with a new incremental learning method based on the regularized orthogonal least square (ROLS) algorithm is proposed for face recognition. It is designed to accommodate new information without retraining the initial network. In our proposed method, the selection of the regressors for the new data is done locally, hence avoiding the expensive reselecting process. In addition, it accumulates previous experience and learns updated new knowledge of the existing groups to increase the robustness of the system. The experimental results show that the proposed method gives higher average recognition accuracy compared to the conventional ROLS-algorithm-based RBF neural network with much lower computational complexity. Furthermore, the proposed method achieves higher recognition accuracy as compared to other incremental learning algorithms such as incremental principal component analysis and incremental linear discriminant analysis in face recognition.
机译：传统的面部识别遭受诸如扩展针对新添加的人的分类器以及学习关于现有人的更新的信息的问题。解决这些问题的方法是重新培训系统，这将需要昂贵的计算复杂性。本文提出了一种基于正则最小二乘（ROLS）算法的径向基函数神经网络（RBF），采用一种新的增量学习方法进行人脸识别。它旨在容纳新信息而无需重新训练初始网络。在我们提出的方法中，为新数据选择回归变量是在本地完成的，因此避免了昂贵的重新选择过程。此外，它积累了以前的经验，并学习了有关现有小组的最新知识，以提高系统的稳定性。实验结果表明，与传统的基于ROLS算法的RBF神经网络相比，该方法具有更高的平均识别精度，且计算复杂度较低。此外，与其他增量学习算法（如面部识别中的增量主成分分析和增量线性判别分析）相比，该方法实现了更高的识别精度。

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