Classification of hand images using geometric features

Personal identification and verification systems are very important for high security and ease of usage in these days. These systems are hand recognition, fingerprint recognition, iris recognition; face recognition which uses biometrics traits. These are the systems with which the entry/ exit of personnel or students in and out of the factories, firms, hospitals and schools are controlled, with which the number of working days and hours are found, in short with which all the information can be obtained.Two classification methods are used in this project. They are k-Nearest Neighbor Algorithm and Linear Discriminant Analysis. The success rate of k-NN Method is 97.0 percent; the success rate of Linear Discriminant Analysis is 97.7 percent for the database containing 16 features with 300 hand images obtained from 60 people.The results were obtained by different methods and algorithm, and a table showing the minimum error rates according to the method used, was prepared. Results were compared. The best method for hand recognition system was decided.