Atuomated identity verification based on feature points matching of hand shapes

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.