肤色信息马氏图的RBPNN人脸识别

Face Recognition Based on RBPNN of Mahalanobis Distance Map for Skin Color Information

根据肤色信息在YCbCr空间分布特点,提出在基于肤色信息的马氏距离图的特征脸空间中用RBPNN神经网络进行人脸识别。该方法利用肤色信息构造图像的马氏距离图,利用K-L变换构造特征脸空间。在特征脸空间中提取图像的统计特征,以这些统计特征作为输入,构造径向基概率神经网络,利用它的非线性计算和映射能力,进行人脸识别与分类。实验证明,这种方法能够有效地完成人脸识别。

According to skin color distribution in YCbCr color space, a novel face recognition method based on radial basis probabilistic neural network （RBPNN） in Eigenface Space of Mahalanobis Distance Map to Skin Color Information was proposed. Mahalanobis distance map of image was set up with skin color information, and Eigenface space was constructed depending on K-L transformation. In Eigenface space, statistical characteristics of image were extracted, which were served as inputs of neural network. Radial basis probabilistic neural network was constructed in terms of its nonlinear computation and mapping, and used to recognize and classify face image. Experimental result Shows the approach is effective to identify.