Palmprint classification using principal lines
This paper proposes a novel algorithm for the automatic classification of low-resolution palmprints. First the principal lines of the palm are defined using their position and thickness. Then a set of directional line detectors is devised. After that we use these directional line detectors to extract the principal lines in terms of their characteristics and their definitions in two steps: the potential beginnings (line initials) of the principal lines are extracted and then, based on these line initials, a recursive process is applied to extract the principal lines in their entirety. Finally palmprints are classified into six categories according to the number of the principal lines and the number of their intersections. The proportions of these six categories (16) in our database containing 13,800 samples are 0.36%, 1.23%, 2.83%, 11.81%, 78.12% and 5.65%, respectively. The proposed algorithm has been shown to classify palmprints with an accuracy of 96.03%.
palmprint classification principal lines biometrics life line head line heart line