摘要:
该文提出了一种基于手形的身份认证方法。该方法首先通过对手形图像的处理,将手形表示为由一系列有序点构成的特征点集,然后应用基于确定性退火技术的点匹配算法对两个手形的特征点集进行匹配,得到用于认证的两个匹配参数:平均匹配误差和匹配率,最后设计适当的分类器,对匹配结果进行分类判决,实现身份认证。考虑到在手形认证的研究中都是小样本情况,因此首次将建立在统计学习理论(SLT)基础之上的支持向量机(SVM)应用于手形的认证中,得到的结果是令人满意的。实验证明,与现有的手形认证方法相比,该文的方法不仅提高了认证的准确性,而且增强了认证的鲁棒性。
Abstract:
This paper presents a method for identity verification based on matching of hand shapes. The method first represents the shapes of hands by sets of ordered points. Next, the two sets of points are matched using point matching algorithm based on deterministic annealing and get the two matching parameters: mean matching error and matching rate. Finally, the classifier is designed for classification/verification. Considering the research of hand shape verification usually works in practical cases of limited or small samples, Support Vector Machine (SVM) is developed for verification. SVM is a new technique in the field of Statistical Learning Theory (SLT). The preliminary results show that the method can obtain higher levels of accuracy and robustness than the existing systems that based on hand geometry measurements.
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