融合双手几何特征，用证据理论提升身份识别准确率
Fusion Based Hand Geometry Recognition Using Dempster-Shafer Theory
- 双侧手部图像提取特征，多级融合提升识别鲁棒性
- 识别准确率达99.5%，验证时误接受率仅0.625%
- 适合无姿态限制的高安全生物识别场景
本文提出一种基于双侧手部几何特征融合的身份识别新方法，无需姿态约束。所有特征从归一化左右手图像中提取，分别在特征层和决策层进行融合。提出两种基于概率的分类算法：第一种计算最近三个邻居的最大概率；第二种根据匹配特征数与距离阈值确定最大概率。基于这两个最高概率生成初始决策，最终决策由达摩斯特-沙弗证据理论计算最高概率得出。针对201名受试者，实验了三种初始决策组合方案，识别正确率高达99.5%，验证时误接受率（FAR）为0.625%。
原文摘要 · Abstract (English)
This paper presents a new technique for person recognition based on the fusion of hand geometric features of both the hands without any pose restrictions. All the features are extracted from normalized left and right hand images. Fusion is applied at feature level and also at decision level. Two probability based algorithms are proposed for classification. The first algorithm computes the maximum probability for nearest three neighbors. The second algorithm determines the maximum probability of the number of matched features with respect to a thresholding on distances. Based on these two highest probabilities initial decisions are made. The final decision is considered according to the highest probability as calculated by the Dempster-Shafer theory of evidence. Depending on the various combinations of the initial decisions, three schemes are experimented with 201 subjects for identification and verification. The correct identification rate found to be 99.5%, and the False Acceptance Rate (FAR) of 0.625% has been found during verification.
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