Improvement of palmprint coding techniques with feature descriptors

In recent years, biometric palmprint recognition system is one of the preferred research areas due to reach high accuracy even with the low resolution cameras. In recent studies conducted in this area, especially due to hygiene problems, images are taken in contactless and unrestricted environment. Despite a very successful improving methods have been developed for the images obtained in the restricted environment, shifts or projective distortions that occur in the palm pattern obtained from images taken in unconstrained environments negatively affect the success of this method. In this study, coding techniques that are commonly used palmprint recognition systems (Competitive Code, Ordinal Code, Derivative of Gaussian Filters Code) are strengthened with feature determinants (SIFT, SURF) and a new approach has been proposed. Scope of the study, a new data set consists of low-resolution images taken in unconstrained environments created and palmprint patterns obtained with the help of Active Appearance Model. First, performance rate of these coding techniques on the patterns were analyzed. Then, the advantages of 6 different approaches obtained by combining each feature determinant with each coding technique, is disclosed to the system.