Application of Fuzzy Algorithm Based on Similarity Degree in Hand-Shape Identification

The hand-shape identity recognition that makes identification through a way-to extract the parameters of hand contour and recognize the different between the new hand-shape inputted just now and the standard one stored in the database early is a kind of pattern recognition system based on eigenvector. The fuzzy pattern recognition algorithm is often used due to the errors brought from the hand-shape eigenvector extraction process, which cause a certain degree of uncertainty both of the standard hand-shape eigenvectors stored in the database and the one of the recognized object. This thesis discusses the designing and implementation of the fuzzy pattern recognition algorithm based on Lattice Similarity Degree. It turns the process of hand-shape identity recognition into an issue of the similarity degree of two fuzzy sets. This algorithm can be promoted and applied to other pattern recognition system.