ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (1): 151-162.doi: 10.7544/issn1000-1239.2018.20160675

• 人工智能 • 上一篇    下一篇

基于分类距离分数的自适应多模态生物特征融合

张露1,2,王华彬1,2,陶亮1,周健1,2   

  1. 1(计算智能与信号处理教育部重点实验室(安徽大学) 合肥 230601);2(安徽大学媒体计算研究所 合肥 230601) (zldiol@163.com)
  • 出版日期: 2018-01-01
  • 基金资助: 
    国家自然科学基金项目(61372137,61302191);安徽大学信息保障技术协同创新中心开放课题(ADXXBZ201411);安徽大学大学生科研训练计划项目(KYXL201530)

Adaptive Multibiometric Feature Fusion Based on Classification Distance Score

Zhang Lu1,2, Wang Huabin1,2, Tao Liang1, Zhou Jian1,2   

  1. 1(Key Laboratory of Intelligent Computing and Signal Processing (Anhui University), Ministry of Education, Hefei 230601);2(Institute of Media Computing, Anhui University, Hefei 230601)
  • Online: 2018-01-01

摘要: 匹配分数是传统的融合分数指标,但是其不能很好地区分类内和类间数据,分类置信度虽然可以较好地将类内类间数据分开,但对于匹配分数仅次于分类阈值的数据,其分类效果不是很理想.因此,首先提出了一种基于分类距离分数的融合分数指标,其不仅携带一级分类信息,也含有匹配分数与分类阈值之间的距离信息,可增大融合后类内类间分数之间的距离,为融合算法提供了一个具有有效判别信息的特征融合集,提高了融合指标的利用率;进一步,利用信息熵表示信息价值多少的这一特性,定义特征关联系数和特征权重系数,并将加权融合和传统SUM规则统一在一个自适应算法框架中,提高了融合识别率.实验结果验证了所提出方法的有效性.

关键词: 多模态识别技术, 特征融合, 分类距离分数, 信息熵, 自适应融合

Abstract: Matching score is one of the traditional fusion score metrics, but it’s not a good metric to classify the data with intra-class and inter-class scores. The classification confidence score can be used to well separate the data with intra-class score from the data with inter-class score, but it does not work well for the data whose matching scores are next to the classification threshold. Therefore, this paper proposes a new score metric based on the classification distance score, which contains not only the information of the first level of classification but also the information of the distance between matching score and classification threshold, and which can also increase the distance of the fusion scores between intra-class and inter-class scores, and the classification distance score provides the characteristics of effective discriminative information fusion set for fusion algorithm, which can improve the utilization rate of score metric; furthermore, since the information entropy indicates the information value of features, it can be used to define the feature correlation coefficient and feature weight coefficient, and then the weighted fusion and traditional SUM rules are unified in an adaptive algorithm framework, which can improve the fusion recognition rate. The experimental results indicate the validity of the proposed method.

Key words: the multi modal identification technology, feature fusion, classification distance score, information entropy, adaptive fusion

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