A Minutiae Selection Algorithm (MSA) for efficient palmprint matching using Histograms of Differences (HoDs).

Palmprints have gained enormous popularity in the biometric industry recently. Due to the large amount of information provided by the palmprints and their forensic value, many large-scale identification systems are employing high resolution palmprint-based biometric systems with minutiae as the primary identification feature. Minutiae are the palm ridge endings or bifurcations that are represented by their location and orientation, i.e., (x , y , θ). Due to the large size of palmprints, approximately 1000 minutiae are extracted per palmprint on average. Besides the large number, another problem with the extracted minutiae is that a considerable amount is false. Besides increasing the number of minutiae matches between palmprints, these false minutiae reduce matching accuracy. In order to reduce minutiae matches and eliminate false minutia, previous studies have adopted multifaceted approaches such as associating a quality or confidence factor with each minutia, image registration, or inventing computationally efficient minutia descriptors. However, all these approaches require associating each minutia with additional parameters. In this paper, we propose a simple and intuitive histogram-based minutiae selection algorithm (MSA) using only the basic properties, i.e., (x , y , θ) to shortlist a subset of best minutiae candidates for matching. This provides the dual benefit of: (1) a reduced number of minutiae matches between palmprints, and (2) improved matching accuracy through the elimination of false minutiae. The proposed method does not require estimating any additional parameters for minutiae and is independent of minutia encoding and matching methods used. Our results are acquired on a popular and challenging high resolution palmprint dataset and show the efficacy of the proposed algorithm through a clear improvement in matching accuracy and a significant reduction in the number of required minutiae matches between palmprints. • Minutiae selection algorithm to pick best candidate minutiae for palmprint matching. • MSA reduces minutiae matches required and improves matching accuracy. • MSA is independent of minutiae encoding and matching methods. • MSA does not require prior image alignment or image registration between palmprints. • MSA is suitable for application at both offline and online stages. [ABSTRACT FROM AUTHOR]