基于Stein-Weiss函数的彩色掌纹特征识别算法

针对目前掌纹识别算法中对彩色掌纹图像的识别研究不多，提出一种新的基于Stein-Weiss函数解析性质的BP神经网络彩色掌纹图像的识别算法。首先为彩色掌纹图像中的每个像素点构建一个Stein-Weiss函数，再根据Stein-Weiss函数的解析性，计算出相应像素的十六个特征值，将这些特征值输入到BP神经网络的输入层，通过BP神经网络的自学习能力对这些数据进行分类学习；然后通过BP神经网络的泛化能力来获取掌纹边缘线；最后对掌纹边缘线提取成对几何特征建立特征库，通过成对几何直方图相交算法进行掌纹识别。实验结果表明，相对于以往的灰度掌纹图像识别算法，该算法能够更快地提取出更精细的掌纹线，识别率更高，并且对于旋转和噪声的干扰具有较强的鲁棒性。

Color palmprint feature recognition algorithm based on Stein-Weiss function

For the problem of the palmprint recognition algorithm had less research on the recognition of color palmprint images, this paper proposed a new BP neural network color palmprint image recognition algorithm based on the analytical properties of Stein-Weiss function. The proposed method firstly constructed a Stein-Weiss function for each pixel in the color palmprint image. Then according to the analyticity of the Stein-Weiss function, it calculated sixteen eigenvalues of the corresponding pixel, and used these eigenvalues as the input data of the BP neural network. These data could be classified and learned through the self-learning ability of BP neural network. Then it obtained the palmprint edge through generalization ability of BP neural network. Finally, it extracted the pairwise attributes from palmprint edge, and built the feature library. The palmprint was recognized by the paired geometric histogram intersection algorithm. The experimental results show that the proposed algorithm can extract finer palmprint faster and the recognition rate is higher compared with the previous grayscale palmprint image recognition algorithm. And it has strong robustness to the interference of rotation and noise.