Palmprint identification using palmcodes
This paper investigates a new approach for the palmprint identification using real Gabor function (RGF) filtering. Inkless composite hand images have been used to automatically to extract the palmprints from peg-free imaging setup. These palmprints, after normalization, are subjected to selective feature sampling by a bank of RGF. Each of these filtered images has been used to extract significant features (PalmCode) from each of 6 concentric circular bands. Our preliminary experimental results using 400 low-resolution palmprint images achieve the recognition rate of 97.50% and also illustrate the shortcomings of results presented in earlier work. The results show the uniqueness of palmprint texture, even in the two hands of an individual and its possible use in biometrics based personal recognition.