基于信号质量动态加权的多模生物特征识别研究

Multi-biometrics fusion based on dynamic weighting of characteristic signal quality

将人脸识别和说话人识别进行决策层级的融合,为应对外界环境对识别结果的影响,引入图像质量和声音质量评价方法,通过对信息质量进行评估,去除信息质量较差的特征,根据信息质量动态调整模块的权重比例,并对单模特征识别匹配度低的个体做拒绝处理,然后根据D-S理论将各个证据合并成为一个新的证据体,实现对用户身份识别。实验结果显示,这种考虑特征信号质量的融合方法可以有效提升识别的准确率和安全性。

Face recognition and speaker recognition were integrated at the decision-making level. In order to cope with the influence of the external environment on the recognition result, image quality and sound quality assessment methods were introduced. By evaluating the quality of information, features with poor information quality were re- moved. Information quality dynamically adjusted the weight proportion of the module, and rejected individuals with low matching degree of single-mode feature recognition. Then, according to DS theory, each evidence was merged into a new body of evidence to realize user identification. The experimental results show that the fusion method which takes into account the characteristic signal quality can effectively improve the recognition accuracy and security.