特征级生物特征融合：Doddingtons漫画的身份识别
Feature Level Fusion of Biometrics Cues: Human Identification with Doddingtons Caricature
摘要：
本文提出了一种多模式的指纹和耳朵生物识别系统。基于尺度不变特征变换（SIFT）描述子对人耳和指纹提取的特征集进行融合.融合集采用K-中心点分割方法进行编码，减少了特征点的个数。K-medoids将整个数据集划分为聚类，以最小化属于聚类的数据点与其中心之间的误差。缩减特征集用于两个生物特征集之间的匹配。匹配分数使用Doddington引入的基于用户的特征加权方案生成。该技术进行了测试，以展示其强大的性能。
英文摘要：
This paper presents a multimodal biometric system of fingerprint and ear biometrics. Scale Invariant Feature Transform (SIFT) descriptor based feature sets extracted from fingerprint and ear are fused. The fused set is encoded by K-medoids partitioning approach with less number of feature points in the set. K-medoids partition the whole dataset into clusters to minimize the error between data points belonging to the clusters and its center. Reduced feature set is used to match between two biometric sets. Matching scores are generated using wolf-lamb user-dependent feature weighting scheme introduced by Doddington. The technique is tested to exhibit its robust performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
