一种二维鲁棒随机权网络及其应用

A novel two dimension robust neural networks with random weights and its applications

二维随机权网络的主要特点是将矩阵数据直接作为输入,可以保留矩阵数据本身的结构信息,从而提高识别率.然而,二维随机权网络在处理含有离群值的人脸图像识别问题时效果往往不佳.为了解决该问题,提出一种新的人脸识别方法——二维鲁棒随机权网络,并用期望最大化算法来求解网络参数.实验结果显示,该方法能够较好地处理含有离群值的人脸识别问题.

The major advantage of two dimensional neural networks with random weights （2DNNRW） is to use matrix data as the input directly to reserve the structural information of the matrix data itself. Hence, compared with the neural networks with random weights （NNRW）, the recognition rate is improved. However, the existing 2DNNRW is not good at the face recognition with outliers. Now, we proposed a two dimension robust neural networks with random weights （2DRNNRW）. The expectation-maximization algorithm （EM） was used to calculate the parameters of the networks. Experiments on different face databases demonstrate that the proposed algorithm is effective to deal with the problem of face recognition with outliers.