基于信号质量动态加权的多模生物特征识别研究

doi:10.11959/j.issn.2096-109x.2018022

Abstract

Abstract:

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 removed.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.

Key words: face recognition, speaker recognition, characterization of signal quality, D-S evidence theory

CLC Number:

TP391.41

Wenbing ZHANG,Peishun LIU,Fenghui XUE. Multi-biometrics fusion based on dynamic weighting of characteristic signal quality[J]. Chinese Journal of Network and Information Security, 2018, 4(3): 59-67.

Figures/Tables 13

References 38

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图像序号	质量指标
图3(a)	0.79
图3(b)	0.58
图3(c)	0.30
图3(d)	0.18

人脸\声音	归一化后匹配分数α_i	特征信息质量Q_i	结果可信系数R_i	赋值概率函数	目标身份概率
待测身份1	0.21\0.80	0.35\0.96	0.08\0.92	0.11\0.92	0.57
待测身份2	0.06\0.69	0.53\0.78	0.05\0.94	0.03\0.91	0.25
待测身份3	0.85\0.10	0.67\0.48	0.96\0.04	0.95\0.03	0.38
待测身份4	0.08\0.17	—	—	—	—

AUC值	本文方法	不考虑特征质量融合	说话人识别	人脸识别
Q_F∈[0.5,0.7]、Q_S<0.4	0.947 4	0.855 7	0.762 3	0.914 3
Q_S∈[0.5,0.7]、Q_F<0.4	0.932 7	0.819 2	0.876 1	0.737 3
Q_S<0.3、Q_F<0.3	0.874 4	0.723 3	0.627 8	0.731 3

识别方式	时间/s
本文方法	16.81
说话人识别	16.58
人脸识别	15.37

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

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