基于BP-AdaBoost神经网络的多参数掌静脉图像质量评价法

手掌静脉纹识别技术作为新一代高精度的生物特征识别技术，被广泛用于个人身份鉴定领域.然而，其识别效果受限于图像的质量，低质量的图像往往造成识别准确度偏低，如何有效的对图像质量进行评价从而筛选出高质量的图像成为掌静脉识别技术中的一项重要研究内容.本文旨在解决这一问题，提出了一种基于BP-AdaBoost神经网络的多参数的掌静脉图像质量评价法.根据掌静脉图像质量特点，提出多个参数的评价指标（对比度（contrast）、信息熵（entropy）、清晰度（sharpness）和等效视数（enl））.利用BP网络优良的非线性拟合特点，以多个评价参数为网络输入，分类结果为网络输出，训练10个BP弱分类器；在此基础上利用AdaBoost算法得到最终的强分类器.实验结果显示，对比传统加权融合的评价分类方法，分类的结果准确度较高，系统具有具有良好的应用价值.

Multi-Parameter Palm Vein Image Quality Evaluation Method Based on BP-AdaBoost Neural Network

As a new generation of high-precision biometric recognition technology, palm vein pattern recognition technology is widely used in the field of personal identification. However, its recognition effect is limited by the quality of the image. Low-quality images often result in low recognition accuracy. How to effectively evaluate the image quality and screen out high-quality images becomes an important research issue in palm vein recognition technology. This study aims to solve this problem and proposes a multi-parameter palm vein image quality evaluation method based on BP-AdaBoost neural network. According to the quality characteristics of the palm vein image, the evaluation indexes (contrast, entropy, sharpness and equivalent visual number (enl)) of multiple parameters are proposed. Based on the excellent nonlinear fitting characteristics of BP network, multiple evaluation parameters are used as network input, the classification result is network output, and 10 BP weak classifiers are trained. On this basis, the final strong classifier is obtained by AdaBoost algorithm. The experimental results show that compared with the traditional weighted fusion evaluation classification method, the classification results have higher accuracy and the system has good application value.